diff --git a/gen2-mediapipe-selfiesegmentation/conversion/selfie_segmentation_landscape.xml b/gen2-mediapipe-selfiesegmentation/conversion/selfie_segmentation_landscape.xml
new file mode 100644
index 00000000..5b57cfea
--- /dev/null
+++ b/gen2-mediapipe-selfiesegmentation/conversion/selfie_segmentation_landscape.xml
@@ -0,0 +1,7966 @@
+<?xml version="1.0" ?>
+<net name="selfie_segmentation_landscape" version="10">
+	<layers>
+		<layer id="0" name="input_1:0" type="Parameter" version="opset1">
+			<data element_type="f16" shape="1, 3, 144, 256"/>
+			<output>
+				<port id="0" names="input_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>144</dim>
+					<dim>256</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="input_1:0/scale_input_port_1/value161013116" type="Const" version="opset1">
+			<data element_type="f16" offset="0" shape="1, 1, 1, 1" size="2"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="input_1:0/scale" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>144</dim>
+					<dim>256</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>144</dim>
+					<dim>256</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Conv2D/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="34" shape="16, 3, 3, 3" size="864"/>
+			<output>
+				<port id="0" names="Conv2D/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="Conv__158/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>144</dim>
+					<dim>256</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="Conv__158/Dims3503" type="Const" version="opset1">
+			<data element_type="f16" offset="898" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="Conv__158" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__158:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="mul" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="mul_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Conv2D_2/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="930" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_2/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="Conv__161/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="Conv__161/Dims3461" type="Const" version="opset1">
+			<data element_type="f16" offset="1442" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="Conv__161" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__161:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="re_lu" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="const_fold_opt__377" type="Const" version="opset1">
+			<data element_type="f16" offset="1474" shape="16, 1, 1, 3, 3" size="288"/>
+			<output>
+				<port id="0" names="const_fold_opt__377" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="Conv__162/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="Conv__162/Dims3407" type="Const" version="opset1">
+			<data element_type="f16" offset="1762" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="Conv__162" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__162:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="re_lu_1" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="352" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="22" name="global_average_pooling2d/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="23" name="global_average_pooling2d/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="24" name="global_average_pooling2d/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="25" name="global_average_pooling2d/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="26" name="354" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="27" name="global_average_pooling2d/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="28" name="global_average_pooling2d/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="29" name="Conv2D_3/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="1818" shape="8, 16, 1, 1" size="256"/>
+			<output>
+				<port id="0" names="Conv2D_3/filter:0" precision="FP16">
+					<dim>8</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="30" name="Conv__165/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>8</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="31" name="Conv__165/Dims3335" type="Const" version="opset1">
+			<data element_type="f16" offset="2074" shape="1, 8, 1, 1" size="16"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="32" name="Conv__165" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__165:0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="33" name="re_lu_2" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_2:0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="34" name="Conv2D_4/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="2090" shape="16, 8, 1, 1" size="256"/>
+			<output>
+				<port id="0" names="Conv2D_4/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="35" name="Conv__166/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="36" name="Conv__166/Dims3437" type="Const" version="opset1">
+			<data element_type="f16" offset="2346" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="37" name="Conv__166" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__166:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="38" name="activation" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="39" name="multiply" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="40" name="Conv2D_5/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="2378" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_5/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="41" name="Conv__167/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="42" name="Conv__167/Dims3551" type="Const" version="opset1">
+			<data element_type="f16" offset="2890" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="43" name="Conv__167" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__167:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="44" name="Conv2D_6/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="2922" shape="72, 16, 1, 1" size="2304"/>
+			<output>
+				<port id="0" names="Conv2D_6/filter:0" precision="FP16">
+					<dim>72</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="45" name="Conv__171/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>72</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="46" name="Conv__171/Dims3299" type="Const" version="opset1">
+			<data element_type="f16" offset="5226" shape="1, 72, 1, 1" size="144"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="47" name="Conv__171" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__171:0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="48" name="re_lu_3" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_3:0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="49" name="const_fold_opt__387" type="Const" version="opset1">
+			<data element_type="f16" offset="5370" shape="72, 1, 1, 3, 3" size="1296"/>
+			<output>
+				<port id="0" names="const_fold_opt__387" precision="FP16">
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="50" name="Conv__172/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="51" name="Conv__172/Dims3455" type="Const" version="opset1">
+			<data element_type="f16" offset="6666" shape="1, 72, 1, 1" size="144"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="52" name="Conv__172" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__172:0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="53" name="re_lu_4" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_4:0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="54" name="Conv2D_7/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="6810" shape="24, 72, 1, 1" size="3456"/>
+			<output>
+				<port id="0" names="Conv2D_7/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="55" name="Conv__173/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>72</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>72</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="56" name="Conv__173/Dims3491" type="Const" version="opset1">
+			<data element_type="f16" offset="10266" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="57" name="Conv__173" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__173:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="58" name="Conv2D_8/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="10314" shape="88, 24, 1, 1" size="4224"/>
+			<output>
+				<port id="0" names="Conv2D_8/filter:0" precision="FP16">
+					<dim>88</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="59" name="Conv__176/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>88</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="60" name="Conv__176/Dims3347" type="Const" version="opset1">
+			<data element_type="f16" offset="14538" shape="1, 88, 1, 1" size="176"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="61" name="Conv__176" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__176:0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="62" name="re_lu_5" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_5:0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="63" name="const_fold_opt__385" type="Const" version="opset1">
+			<data element_type="f16" offset="14714" shape="88, 1, 1, 3, 3" size="1584"/>
+			<output>
+				<port id="0" names="const_fold_opt__385" precision="FP16">
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="64" name="Conv__177/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="65" name="Conv__177/Dims3383" type="Const" version="opset1">
+			<data element_type="f16" offset="16298" shape="1, 88, 1, 1" size="176"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="66" name="Conv__177" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__177:0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="67" name="re_lu_6" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_6:0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="68" name="Conv2D_9/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="16474" shape="24, 88, 1, 1" size="4224"/>
+			<output>
+				<port id="0" names="Conv2D_9/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="69" name="Conv__178/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>88</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>88</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="70" name="Conv__178/Dims3473" type="Const" version="opset1">
+			<data element_type="f16" offset="20698" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="71" name="Conv__178" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__178:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="72" name="add__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="73" name="Conv2D_10/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="20746" shape="96, 24, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_10/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="74" name="Conv__183/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="75" name="Conv__183/Dims3311" type="Const" version="opset1">
+			<data element_type="f16" offset="25354" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="76" name="Conv__183" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__183:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="77" name="mul_2" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_2:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="78" name="mul_3" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_3:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="79" name="const_fold_opt__383" type="Const" version="opset1">
+			<data element_type="f16" offset="25546" shape="96, 1, 1, 5, 5" size="4800"/>
+			<output>
+				<port id="0" names="const_fold_opt__383" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="80" name="Conv__186/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="2, 2" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="81" name="Conv__186/Dims3485" type="Const" version="opset1">
+			<data element_type="f16" offset="30346" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="82" name="Conv__186" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__186:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="83" name="mul_4" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_4:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="84" name="mul_5" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_5:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="85" name="357" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="86" name="global_average_pooling2d_1/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="87" name="global_average_pooling2d_1/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="88" name="global_average_pooling2d_1/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="89" name="global_average_pooling2d_1/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="90" name="359" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="91" name="global_average_pooling2d_1/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="92" name="global_average_pooling2d_1/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="93" name="Conv2D_11/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="30538" shape="24, 96, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_11/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="94" name="Conv__189/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="95" name="Conv__189/Dims3329" type="Const" version="opset1">
+			<data element_type="f16" offset="35146" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="96" name="Conv__189" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__189:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="97" name="re_lu_7" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_7:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="98" name="Conv2D_12/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="35194" shape="96, 24, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_12/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="99" name="Conv__190/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="100" name="Conv__190/Dims3365" type="Const" version="opset1">
+			<data element_type="f16" offset="39802" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="101" name="Conv__190" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__190:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="102" name="activation_1" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="103" name="multiply_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="104" name="Conv2D_13/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="39994" shape="32, 96, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_13/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="105" name="Conv__191/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="106" name="Conv__191/Dims3521" type="Const" version="opset1">
+			<data element_type="f16" offset="46138" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="107" name="Conv__191" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__191:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="108" name="Conv2D_14/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="46202" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_14/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="109" name="Conv__194/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="110" name="Conv__194/Dims3605" type="Const" version="opset1">
+			<data element_type="f16" offset="54394" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="111" name="Conv__194" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__194:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="112" name="mul_6" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_6:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="113" name="mul_7" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_7:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="114" name="const_fold_opt__381" type="Const" version="opset1">
+			<data element_type="f16" offset="54650" shape="128, 1, 1, 5, 5" size="6400"/>
+			<output>
+				<port id="0" names="const_fold_opt__381" precision="FP16">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="115" name="Conv__197/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="116" name="Conv__197/Dims3569" type="Const" version="opset1">
+			<data element_type="f16" offset="61050" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="117" name="Conv__197" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__197:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="118" name="mul_8" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_8:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="119" name="mul_9" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_9:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="120" name="362" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="121" name="global_average_pooling2d_2/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="122" name="global_average_pooling2d_2/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="123" name="global_average_pooling2d_2/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="124" name="global_average_pooling2d_2/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="125" name="364" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="126" name="global_average_pooling2d_2/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="127" name="global_average_pooling2d_2/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_2:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="128" name="Conv2D_15/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="61306" shape="32, 128, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_15/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="129" name="Conv__200/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="130" name="Conv__200/Dims3581" type="Const" version="opset1">
+			<data element_type="f16" offset="69498" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="131" name="Conv__200" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__200:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="132" name="re_lu_8" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_8:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="133" name="Conv2D_16/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="69562" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_16/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="134" name="Conv__201/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="135" name="Conv__201/Dims3557" type="Const" version="opset1">
+			<data element_type="f16" offset="77754" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="136" name="Conv__201" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__201:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="137" name="activation_2" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_2:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="138" name="multiply_2" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_2:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="139" name="Conv2D_17/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="78010" shape="32, 128, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_17/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="140" name="Conv__202/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="141" name="Conv__202/Dims3377" type="Const" version="opset1">
+			<data element_type="f16" offset="86202" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="142" name="Conv__202" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__202:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="143" name="add_1__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_1__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="144" name="Conv2D_18/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="86266" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_18/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="145" name="Conv__205/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="146" name="Conv__205/Dims3413" type="Const" version="opset1">
+			<data element_type="f16" offset="94458" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="147" name="Conv__205" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__205:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="148" name="mul_10" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_10:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="149" name="mul_11" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_11:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="150" name="const_fold_opt__379" type="Const" version="opset1">
+			<data element_type="f16" offset="94714" shape="128, 1, 1, 5, 5" size="6400"/>
+			<output>
+				<port id="0" names="const_fold_opt__379" precision="FP16">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="151" name="Conv__208/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="152" name="Conv__208/Dims3599" type="Const" version="opset1">
+			<data element_type="f16" offset="101114" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="153" name="Conv__208" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__208:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="154" name="mul_12" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_12:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="155" name="mul_13" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_13:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="156" name="367" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="157" name="global_average_pooling2d_3/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="158" name="global_average_pooling2d_3/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="159" name="global_average_pooling2d_3/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="160" name="global_average_pooling2d_3/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="161" name="369" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="162" name="global_average_pooling2d_3/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="163" name="global_average_pooling2d_3/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_3:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="164" name="Conv2D_19/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="101370" shape="32, 128, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_19/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="165" name="Conv__211/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="166" name="Conv__211/Dims3515" type="Const" version="opset1">
+			<data element_type="f16" offset="109562" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="167" name="Conv__211" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__211:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="168" name="re_lu_9" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_9:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="169" name="Conv2D_20/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="109626" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_20/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="170" name="Conv__212/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="171" name="Conv__212/Dims3593" type="Const" version="opset1">
+			<data element_type="f16" offset="117818" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="172" name="Conv__212" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__212:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="173" name="activation_3" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_3:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="174" name="multiply_3" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_3:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="175" name="Conv2D_21/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="118074" shape="32, 128, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_21/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="176" name="Conv__213/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="177" name="Conv__213/Dims3359" type="Const" version="opset1">
+			<data element_type="f16" offset="126266" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="178" name="Conv__213" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__213:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="179" name="add_2__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_2__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="180" name="Conv2D_22/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="126330" shape="96, 32, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_22/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="181" name="Conv__216/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="182" name="Conv__216/Dims3449" type="Const" version="opset1">
+			<data element_type="f16" offset="132474" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="183" name="Conv__216" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__216:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="184" name="mul_14" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_14:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="185" name="mul_15" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_15:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="186" name="const_fold_opt__395" type="Const" version="opset1">
+			<data element_type="f16" offset="132666" shape="96, 1, 1, 5, 5" size="4800"/>
+			<output>
+				<port id="0" names="const_fold_opt__395" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="187" name="Conv__219/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="188" name="Conv__219/Dims3575" type="Const" version="opset1">
+			<data element_type="f16" offset="137466" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="189" name="Conv__219" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__219:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="190" name="mul_16" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_16:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="191" name="mul_17" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_17:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="192" name="372" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="193" name="global_average_pooling2d_4/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="194" name="global_average_pooling2d_4/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="195" name="global_average_pooling2d_4/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="196" name="global_average_pooling2d_4/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="197" name="374" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="198" name="global_average_pooling2d_4/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="199" name="global_average_pooling2d_4/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_4:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="200" name="Conv2D_23/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="137658" shape="24, 96, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_23/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="201" name="Conv__222/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="202" name="Conv__222/Dims3509" type="Const" version="opset1">
+			<data element_type="f16" offset="142266" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="203" name="Conv__222" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__222:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="204" name="re_lu_10" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_10:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="205" name="Conv2D_24/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="142314" shape="96, 24, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_24/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="206" name="Conv__223/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="207" name="Conv__223/Dims3293" type="Const" version="opset1">
+			<data element_type="f16" offset="146922" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="208" name="Conv__223" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__223:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="209" name="activation_4" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_4:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="210" name="multiply_4" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_4:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="211" name="Conv2D_25/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="147114" shape="32, 96, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_25/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="212" name="Conv__224/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="213" name="Conv__224/Dims3317" type="Const" version="opset1">
+			<data element_type="f16" offset="153258" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="214" name="Conv__224" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__224:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="215" name="add_3__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_3__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="216" name="Conv2D_26/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="153322" shape="96, 32, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_26/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="217" name="Conv__227/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="218" name="Conv__227/Dims3563" type="Const" version="opset1">
+			<data element_type="f16" offset="159466" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="219" name="Conv__227" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__227:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="220" name="mul_18" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_18:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="221" name="mul_19" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_19:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="222" name="const_fold_opt__407" type="Const" version="opset1">
+			<data element_type="f16" offset="159658" shape="96, 1, 1, 5, 5" size="4800"/>
+			<output>
+				<port id="0" names="const_fold_opt__407" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="223" name="Conv__230/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>5</dim>
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="224" name="Conv__230/Dims3497" type="Const" version="opset1">
+			<data element_type="f16" offset="164458" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="225" name="Conv__230" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__230:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="226" name="mul_20" type="HSigmoid" version="opset5">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="mul_20:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="227" name="mul_21" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="mul_21:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="228" name="377" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="229" name="global_average_pooling2d_5/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="230" name="global_average_pooling2d_5/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="231" name="global_average_pooling2d_5/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="232" name="global_average_pooling2d_5/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="233" name="379" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="234" name="global_average_pooling2d_5/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="235" name="global_average_pooling2d_5/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_5:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="236" name="Conv2D_27/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="164650" shape="24, 96, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_27/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="237" name="Conv__233/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="238" name="Conv__233/Dims3545" type="Const" version="opset1">
+			<data element_type="f16" offset="169258" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="239" name="Conv__233" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__233:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="240" name="re_lu_11" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_11:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="241" name="Conv2D_28/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="169306" shape="96, 24, 1, 1" size="4608"/>
+			<output>
+				<port id="0" names="Conv2D_28/filter:0" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="242" name="Conv__234/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>96</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="243" name="Conv__234/Dims3425" type="Const" version="opset1">
+			<data element_type="f16" offset="173914" shape="1, 96, 1, 1" size="192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="244" name="Conv__234" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__234:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="245" name="activation_5" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_5:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="246" name="multiply_5" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_5:0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="247" name="Conv2D_29/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="174106" shape="32, 96, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_29/filter:0" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="248" name="Conv__235/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>96</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>96</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="249" name="Conv__235/Dims3323" type="Const" version="opset1">
+			<data element_type="f16" offset="180250" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="250" name="Conv__235" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__235:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="251" name="add_4__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_4__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="252" name="Conv2D_30/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="180314" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_30/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="253" name="Conv__239/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="254" name="Conv__239/Dims3341" type="Const" version="opset1">
+			<data element_type="f16" offset="188506" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="255" name="Conv__239" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__239:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="256" name="re_lu_12" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_12:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="257" name="382" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="258" name="global_average_pooling2d_6/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="259" name="global_average_pooling2d_6/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="260" name="global_average_pooling2d_6/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="261" name="global_average_pooling2d_6/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="262" name="384" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="263" name="global_average_pooling2d_6/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="264" name="global_average_pooling2d_6/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_6:0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="265" name="Conv2D_31/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="188762" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" names="Conv2D_31/filter:0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="266" name="Conv__238/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="267" name="Conv__238/Dims3533" type="Const" version="opset1">
+			<data element_type="f16" offset="196954" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="268" name="Conv__238" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__238:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="269" name="activation_6" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_6:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="270" name="multiply_6" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_6:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="271" name="Upsample__110/ShapeOf" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="272" name="307" type="Convert" version="opset1">
+			<data destination_type="f32"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="273" name="Upsample__110/Add" type="Const" version="opset1">
+			<data element_type="f32" offset="197210" shape="4" size="16"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="274" name="Upsample__110/Mul" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="275" name="Upsample__110/Floor" type="Floor" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="276" name="310" type="Convert" version="opset1">
+			<data destination_type="i64"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="277" name="Upsample__110/sizes_ss_input_port_1/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="278" name="Upsample__110/sizes_ss_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="279" name="Upsample__110/sizes_ss_input_port_3/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="280" name="Upsample__110/sizes_ss" type="StridedSlice" version="opset1">
+			<data begin_mask="0" ellipsis_mask="0" end_mask="1" new_axis_mask="0" shrink_axis_mask="0"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="281" name="Upsample__110/scales_ss" type="Const" version="opset1">
+			<data element_type="f32" offset="197226" shape="2" size="8"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="282" name="Upsample__110/axes_input_port_0/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="283" name="Upsample__110/max_axes/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="284" name="Upsample__110/max_axes/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="285" name="Upsample__110/max_axes/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="286" name="Upsample__110/max_axes" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="287" name="Upsample__110/axes_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="288" name="Upsample__110/axes" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="289" name="Upsample__110" type="Interpolate" version="opset4">
+			<data antialias="false" coordinate_transformation_mode="asymmetric" cube_coeff="-0.75" mode="linear_onnx" nearest_mode="simple" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" shape_calculation_mode="scales"/>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>9</dim>
+					<dim>16</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="2" precision="FP32">
+					<dim>2</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" names="Upsample__110:0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="290" name="Conv2D_32/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="197234" shape="24, 128, 1, 1" size="6144"/>
+			<output>
+				<port id="0" names="Conv2D_32/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="291" name="Conv__240/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="292" name="Conv__240/Dims3539" type="Const" version="opset1">
+			<data element_type="f16" offset="203378" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="293" name="Conv__240" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__240:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="294" name="add_5__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="295" name="387" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="296" name="global_average_pooling2d_7/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="297" name="global_average_pooling2d_7/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="298" name="global_average_pooling2d_7/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="299" name="global_average_pooling2d_7/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="300" name="389" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="301" name="global_average_pooling2d_7/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="302" name="global_average_pooling2d_7/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_7:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="303" name="Conv2D_33/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="203426" shape="24, 24, 1, 1" size="1152"/>
+			<output>
+				<port id="0" names="Conv2D_33/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="304" name="Conv__243/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="305" name="Conv__243/Dims3287" type="Const" version="opset1">
+			<data element_type="f16" offset="204578" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="306" name="Conv__243" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__243:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="307" name="re_lu_13" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_13:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="308" name="Conv2D_34/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="204626" shape="24, 24, 1, 1" size="1152"/>
+			<output>
+				<port id="0" names="Conv2D_34/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="309" name="Conv__244/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="310" name="Conv__244/Dims3305" type="Const" version="opset1">
+			<data element_type="f16" offset="205778" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="311" name="Conv__244" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__244:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="312" name="activation_7" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_7:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="313" name="multiply_7" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_7:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="314" name="add_6__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_6__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="315" name="Conv2D_35/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="205826" shape="24, 24, 1, 1" size="1152"/>
+			<output>
+				<port id="0" names="Conv2D_35/filter:0" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="316" name="Conv__245/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>24</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="317" name="Conv__245/Dims3587" type="Const" version="opset1">
+			<data element_type="f16" offset="206978" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="318" name="Conv__245" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__245:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="319" name="re_lu_14" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_14:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="320" name="const_fold_opt__394" type="Const" version="opset1">
+			<data element_type="f16" offset="207026" shape="24, 1, 1, 3, 3" size="432"/>
+			<output>
+				<port id="0" names="const_fold_opt__394" precision="FP16">
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="321" name="Conv__248/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="322" name="Conv__248/Dims3371" type="Const" version="opset1">
+			<data element_type="f16" offset="207458" shape="1, 24, 1, 1" size="48"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="323" name="Conv__248" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__248:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="324" name="re_lu_15" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_15:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="325" name="add_7__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_7__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="326" name="Upsample__126/ShapeOf" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="327" name="327" type="Convert" version="opset1">
+			<data destination_type="f32"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="328" name="Upsample__126/Add" type="Const" version="opset1">
+			<data element_type="f32" offset="197210" shape="4" size="16"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="329" name="Upsample__126/Mul" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="330" name="Upsample__126/Floor" type="Floor" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="331" name="330" type="Convert" version="opset1">
+			<data destination_type="i64"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="332" name="Upsample__126/sizes_ss_input_port_1/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="333" name="Upsample__126/sizes_ss_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="334" name="Upsample__126/sizes_ss_input_port_3/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="335" name="Upsample__126/sizes_ss" type="StridedSlice" version="opset1">
+			<data begin_mask="0" ellipsis_mask="0" end_mask="1" new_axis_mask="0" shrink_axis_mask="0"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="336" name="Upsample__126/scales_ss" type="Const" version="opset1">
+			<data element_type="f32" offset="197226" shape="2" size="8"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="337" name="Upsample__126/axes_input_port_0/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="338" name="Upsample__126/max_axes/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="339" name="Upsample__126/max_axes/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="340" name="Upsample__126/max_axes/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="341" name="Upsample__126/max_axes" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="342" name="Upsample__126/axes_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="343" name="Upsample__126/axes" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="344" name="Upsample__126" type="Interpolate" version="opset4">
+			<data antialias="false" coordinate_transformation_mode="asymmetric" cube_coeff="-0.75" mode="linear_onnx" nearest_mode="simple" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" shape_calculation_mode="scales"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>18</dim>
+					<dim>32</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="2" precision="FP32">
+					<dim>2</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" names="Upsample__126:0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="345" name="Conv2D_36/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="207506" shape="16, 24, 1, 1" size="768"/>
+			<output>
+				<port id="0" names="Conv2D_36/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="346" name="Conv__249/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>24</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>24</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="347" name="Conv__249/Dims3395" type="Const" version="opset1">
+			<data element_type="f16" offset="208274" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="348" name="Conv__249" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__249:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="349" name="add_8__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="350" name="392" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="351" name="global_average_pooling2d_8/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="352" name="global_average_pooling2d_8/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="353" name="global_average_pooling2d_8/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="354" name="global_average_pooling2d_8/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="355" name="394" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="356" name="global_average_pooling2d_8/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="357" name="global_average_pooling2d_8/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_8:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="358" name="Conv2D_37/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="208306" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_37/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="359" name="Conv__252/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="360" name="Conv__252/Dims3401" type="Const" version="opset1">
+			<data element_type="f16" offset="208818" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="361" name="Conv__252" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__252:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="362" name="re_lu_16" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_16:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="363" name="Conv2D_38/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="208850" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_38/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="364" name="Conv__253/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="365" name="Conv__253/Dims3527" type="Const" version="opset1">
+			<data element_type="f16" offset="209362" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="366" name="Conv__253" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__253:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="367" name="activation_8" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_8:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="368" name="multiply_8" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="multiply_8:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="369" name="add_9__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_9__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="370" name="Conv2D_39/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="209394" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_39/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="371" name="Conv__254/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="372" name="Conv__254/Dims3389" type="Const" version="opset1">
+			<data element_type="f16" offset="209906" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="373" name="Conv__254" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__254:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="374" name="re_lu_17" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_17:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="375" name="const_fold_opt__391" type="Const" version="opset1">
+			<data element_type="f16" offset="209938" shape="16, 1, 1, 3, 3" size="288"/>
+			<output>
+				<port id="0" names="const_fold_opt__391" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="376" name="Conv__257/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="377" name="Conv__257/Dims3467" type="Const" version="opset1">
+			<data element_type="f16" offset="210226" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="378" name="Conv__257" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__257:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="379" name="re_lu_18" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_18:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="380" name="add_10__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_10__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="381" name="Upsample__142/ShapeOf" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="382" name="347" type="Convert" version="opset1">
+			<data destination_type="f32"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="383" name="Upsample__142/Add" type="Const" version="opset1">
+			<data element_type="f32" offset="197210" shape="4" size="16"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="384" name="Upsample__142/Mul" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="385" name="Upsample__142/Floor" type="Floor" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="386" name="350" type="Convert" version="opset1">
+			<data destination_type="i64"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="387" name="Upsample__142/sizes_ss_input_port_1/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="388" name="Upsample__142/sizes_ss_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="389" name="Upsample__142/sizes_ss_input_port_3/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="390" name="Upsample__142/sizes_ss" type="StridedSlice" version="opset1">
+			<data begin_mask="0" ellipsis_mask="0" end_mask="1" new_axis_mask="0" shrink_axis_mask="0"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="391" name="Upsample__142/scales_ss" type="Const" version="opset1">
+			<data element_type="f32" offset="197226" shape="2" size="8"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="392" name="Upsample__142/axes_input_port_0/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="393" name="Upsample__142/max_axes/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="394" name="Upsample__142/max_axes/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="395" name="Upsample__142/max_axes/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="396" name="Upsample__142/max_axes" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="397" name="Upsample__142/axes_input_port_2/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="398" name="Upsample__142/axes" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="399" name="Upsample__142" type="Interpolate" version="opset4">
+			<data antialias="false" coordinate_transformation_mode="asymmetric" cube_coeff="-0.75" mode="linear_onnx" nearest_mode="simple" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" shape_calculation_mode="scales"/>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>36</dim>
+					<dim>64</dim>
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+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="2" precision="FP32">
+					<dim>2</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" names="Upsample__142:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="400" name="Conv2D_40/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="210258" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_40/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="401" name="Conv__258/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="402" name="Conv__258/Dims3479" type="Const" version="opset1">
+			<data element_type="f16" offset="210770" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="403" name="Conv__258" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__258:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="404" name="add_11__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="405" name="397" type="Const" version="opset1">
+			<data element_type="i64" offset="1794" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="406" name="global_average_pooling2d_9/input_rank/shape_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="407" name="global_average_pooling2d_9/input_rank/rank_of" type="ShapeOf" version="opset3">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="408" name="global_average_pooling2d_9/input_rank/0d_rank_of/value" type="Const" version="opset1">
+			<data element_type="i64" offset="1802" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="409" name="global_average_pooling2d_9/input_rank" type="Squeeze" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
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+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="410" name="399" type="Const" version="opset1">
+			<data element_type="i64" offset="1810" shape="" size="8"/>
+			<output>
+				<port id="0" precision="I64"/>
+			</output>
+		</layer>
+		<layer id="411" name="global_average_pooling2d_9/global_pooling_reduce_axis" type="Range" version="opset4">
+			<data output_type="i64"/>
+			<input>
+				<port id="0" precision="I64"/>
+				<port id="1" precision="I64"/>
+				<port id="2" precision="I64"/>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="412" name="global_average_pooling2d_9/reduce" type="ReduceMean" version="opset1">
+			<data keep_dims="true"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="global_average_pooling2d_9:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="413" name="Conv2D_41/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="210802" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_41/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="414" name="Conv__261/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="415" name="Conv__261/Dims3431" type="Const" version="opset1">
+			<data element_type="f16" offset="211314" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="416" name="Conv__261" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__261:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="417" name="re_lu_19" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_19:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="418" name="Conv2D_42/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="211346" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_42/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="419" name="Conv__262/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="420" name="Conv__262/Dims3419" type="Const" version="opset1">
+			<data element_type="f16" offset="211858" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="421" name="Conv__262" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__262:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="422" name="activation_9" type="Sigmoid" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="activation_9:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="423" name="multiply_9" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
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+				<port id="1" precision="FP16">
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="multiply_9:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="424" name="add_12__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_12__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="425" name="Conv2D_43/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="211890" shape="16, 16, 1, 1" size="512"/>
+			<output>
+				<port id="0" names="Conv2D_43/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="426" name="Conv__263/WithoutBiases" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>128</dim>
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+				<port id="1" precision="FP16">
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+					<dim>16</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="427" name="Conv__263/Dims3443" type="Const" version="opset1">
+			<data element_type="f16" offset="212402" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="428" name="Conv__263" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__263:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="429" name="re_lu_20" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_20:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="430" name="const_fold_opt__389" type="Const" version="opset1">
+			<data element_type="f16" offset="212434" shape="16, 1, 1, 3, 3" size="288"/>
+			<output>
+				<port id="0" names="const_fold_opt__389" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="431" name="Conv__266/WithoutBiases" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="432" name="Conv__266/Dims3353" type="Const" version="opset1">
+			<data element_type="f16" offset="212722" shape="1, 16, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
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+		<layer id="433" name="Conv__266" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="Conv__266:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="434" name="re_lu_21" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="re_lu_21:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="435" name="add_13__xeno_compat__1" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="add_13__xeno_compat__1:0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>72</dim>
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="436" name="conv2d_transpose/filter:0" type="Const" version="opset1">
+			<data element_type="f16" offset="212754" shape="16, 1, 2, 2" size="128"/>
+			<output>
+				<port id="0" names="conv2d_transpose/filter:0" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>2</dim>
+					<dim>2</dim>
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+			</output>
+		</layer>
+		<layer id="437" name="conv2d_transpose/original_spatial_shape1169412786" type="Const" version="opset1">
+			<data element_type="i64" offset="212882" shape="2" size="16"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="438" name="conv2d_transpose" type="ConvolutionBackpropData" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" output_padding="0, 0" pads_begin="0, 0" pads_end="0, 0" strides="2, 2"/>
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+				<port id="2" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" names="conv2d_transpose:0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>144</dim>
+					<dim>256</dim>
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+			</output>
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+			<data element_type="f16" offset="212898" shape="1, 1, 1, 1" size="2"/>
+			<output>
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+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="440" name="conv2d_transpose_add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
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+					<dim>256</dim>
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+	<meta_data>
+		<MO_version value="2021.4.0-3839-cd81789d294-releases/2021/4"/>
+		<cli_parameters>
+			<caffe_parser_path value="DIR"/>
+			<data_type value="FP16"/>
+			<disable_nhwc_to_nchw value="False"/>
+			<disable_omitting_optional value="False"/>
+			<disable_resnet_optimization value="False"/>
+			<disable_weights_compression value="False"/>
+			<enable_concat_optimization value="False"/>
+			<enable_flattening_nested_params value="False"/>
+			<enable_ssd_gluoncv value="False"/>
+			<extensions value="DIR"/>
+			<framework value="onnx"/>
+			<freeze_placeholder_with_value value="{}"/>
+			<generate_deprecated_IR_V7 value="False"/>
+			<input_model value="DIR/selfie_segmentation_landscape.onnx"/>
+			<input_model_is_text value="False"/>
+			<input_shape value="[1,3,144,256]"/>
+			<k value="DIR/CustomLayersMapping.xml"/>
+			<keep_shape_ops value="True"/>
+			<legacy_ir_generation value="False"/>
+			<legacy_mxnet_model value="False"/>
+			<log_level value="ERROR"/>
+			<mean_scale_values value="{}"/>
+			<mean_values value="()"/>
+			<model_name value="selfie_segmentation_landscape"/>
+			<output_dir value="DIR"/>
+			<placeholder_data_types value="{}"/>
+			<placeholder_shapes value="[  1   3 144 256]"/>
+			<progress value="False"/>
+			<remove_memory value="False"/>
+			<remove_output_softmax value="False"/>
+			<reverse_input_channels value="True"/>
+			<save_params_from_nd value="False"/>
+			<scale value="255.0"/>
+			<scale_values value="()"/>
+			<silent value="False"/>
+			<static_shape value="False"/>
+			<stream_output value="False"/>
+			<transform value=""/>
+			<unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, pretrained_model_name, saved_model_dir, saved_model_tags, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+		</cli_parameters>
+	</meta_data>
+</net>
diff --git a/gen2-mediapipe-selfiesegmentation/gen2-mediapipe-selfiesegmentation.zip b/gen2-mediapipe-selfiesegmentation/gen2-mediapipe-selfiesegmentation.zip
new file mode 100644
index 00000000..b169e642
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diff --git a/gen2-mediapipe-selfiesegmentation/main.py b/gen2-mediapipe-selfiesegmentation/main.py
new file mode 100644
index 00000000..a1c9c6f7
--- /dev/null
+++ b/gen2-mediapipe-selfiesegmentation/main.py
@@ -0,0 +1,108 @@
+import cv2
+import depthai as dai
+import numpy as np
+import argparse
+import time
+
+cam_options = ['rgb', 'left', 'right']
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-cam", "--cam_input", help="select camera input source for inference", default='rgb', choices=cam_options)
+parser.add_argument("-nn", "--nn_model", help="select model path for inference", default='models/selfie_segmentation_landscape_openvino_2021.4_6shave_RGB_interleaved.blob', type=str)
+
+args = parser.parse_args()
+
+cam_source = args.cam_input
+nn_path = args.nn_model
+
+NN_W, NN_H = 256, 144
+
+
+# Start defining a pipeline
+pipeline = dai.Pipeline()
+pipeline.setOpenVINOVersion(version = dai.OpenVINO.Version.VERSION_2021_4)
+
+# Define a neural network that will make predictions based on the source frames
+detection_nn = pipeline.createNeuralNetwork()
+detection_nn.setBlobPath(nn_path)
+
+detection_nn.setNumPoolFrames(4)
+detection_nn.input.setBlocking(False)
+detection_nn.setNumInferenceThreads(2)
+
+cam=None
+# Define a source - color camera
+cam = pipeline.createColorCamera()
+cam.setPreviewSize(NN_W,NN_H)
+cam.setInterleaved(True)
+cam.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
+cam.preview.link(detection_nn.input)
+cam.setFps(50)
+
+# Create outputs
+xout_rgb = pipeline.createXLinkOut()
+xout_rgb.setStreamName("nn_input")
+xout_rgb.input.setBlocking(False)
+
+detection_nn.passthrough.link(xout_rgb.input)
+
+xout_nn = pipeline.createXLinkOut()
+xout_nn.setStreamName("nn")
+xout_nn.input.setBlocking(False)
+
+detection_nn.out.link(xout_nn.input)
+
+# Pipeline defined, now the device is assigned and pipeline is started
+device = dai.Device(pipeline)
+device.startPipeline()
+
+# Output queues will be used to get the rgb frames and nn data from the outputs defined above
+q_nn_input = device.getOutputQueue(name="nn_input", maxSize=4, blocking=False)
+q_nn = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
+
+start_time = time.time()
+counter = 0
+fps = 0
+layer_info_printed = False
+
+while True:
+    # instead of get (blocking) used tryGet (nonblocking) which will return the available data or None otherwise
+    in_nn_input = q_nn_input.get()
+    in_nn = q_nn.get()
+
+    frame = in_nn_input.getCvFrame()
+    lay1 = in_nn.getFirstLayerFp16()
+    pred = np.array(lay1, dtype=np.float16).reshape((NN_H, NN_W))
+    #pred = np.transpose(pred, (1,0))
+
+    condition = np.stack([pred > 0.15] * 3, axis = 2)
+    bg_image = np.zeros(frame.shape, dtype=np.uint8)
+    bg_image[:] = (255, 255, 255)
+    output_image = np.where(condition, frame, bg_image)
+    output_image = output_image.astype(np.uint8)
+
+    # transpose
+    #output_image = np.transpose(output_image, (2, 0, 1))
+    #frame = np.transpose(frame, (2, 0, 1))
+
+
+    color_black, color_white = (0, 0, 0), (255, 255, 255)
+    label_fps = "Fps: {:.2f}".format(fps)
+    (w1, h1), _ = cv2.getTextSize(label_fps, cv2.FONT_HERSHEY_TRIPLEX, 0.4, 1)
+    cv2.rectangle(output_image, (0, output_image.shape[0] - h1 - 6), (w1 + 2, output_image.shape[0]), color_white, -1)
+    cv2.putText(output_image, label_fps, (2, output_image.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX,
+                0.4, color_black)
+
+
+    cv2.imshow("nn_input", frame)
+    cv2.imshow("result", output_image)
+
+    counter+=1
+    if (time.time() - start_time) > 1 :
+        fps = counter / (time.time() - start_time)
+        counter = 0
+        start_time = time.time()
+
+
+    if cv2.waitKey(1) == ord('q'):
+        break
\ No newline at end of file
diff --git a/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape.blob b/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape.blob
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diff --git a/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave (5).blob b/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave (5).blob
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diff --git a/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave.blob b/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave.blob
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diff --git a/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave_RGB_interleaved.blob b/gen2-mediapipe-selfiesegmentation/models/selfie_segmentation_landscape_openvino_2021.4_6shave_RGB_interleaved.blob
new file mode 100644
index 00000000..55fba8a0
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diff --git a/gen2-selfie-segmentation/main.py b/gen2-selfie-segmentation/main.py
new file mode 100755
index 00000000..d04a7817
--- /dev/null
+++ b/gen2-selfie-segmentation/main.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+
+from pathlib import Path
+import cv2
+import depthai as dai
+import numpy as np
+import argparse
+import time
+import sys
+
+cam_options = ['rgb', 'left', 'right']
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-cam", "--cam_input", help="select camera input source for inference", default='rgb', choices=cam_options)
+parser.add_argument("-nn", "--nn_model", help="select model path for inference", default='models/model.blob', type=str)
+
+args = parser.parse_args()
+
+cam_source = args.cam_input
+nn_path = args.nn_model
+
+nn_shape = 256
+
+def decode_deeplabv3p(output):
+    class_colors = [[0,0,0],  [0,255,0]]
+    class_colors = np.asarray(class_colors, dtype=np.uint8)
+
+    # output = output_tensor.reshape(nn_shape,nn_shape)
+    output_colors = np.take(class_colors, output, axis=0)
+    return output_colors
+
+def show_deeplabv3p(output_colors, frame, weight=0.2):
+    return cv2.addWeighted(frame, 1, output_colors,weight,0)
+
+
+
+# Start defining a pipeline
+pipeline = dai.Pipeline()
+
+# pipeline.setOpenVINOVersion(version = dai.OpenVINO.Version.VERSION_2021_2)
+
+# Define a neural network that will make predictions based on the source frames
+detection_nn = pipeline.createNeuralNetwork()
+detection_nn.setBlobPath(nn_path)
+
+detection_nn.setNumPoolFrames(4)
+detection_nn.input.setBlocking(False)
+detection_nn.setNumInferenceThreads(2)
+
+cam=None
+# Define a source - color camera
+cam = pipeline.createColorCamera()
+cam.setPreviewSize(nn_shape,nn_shape)
+cam.setFp16(True)
+cam.setInterleaved(False)
+cam.preview.link(detection_nn.input)
+cam.setFps(50)
+
+# Create outputs
+xout_rgb = pipeline.createXLinkOut()
+xout_rgb.setStreamName("nn_input")
+xout_rgb.input.setBlocking(False)
+
+detection_nn.passthrough.link(xout_rgb.input)
+
+xout_nn = pipeline.createXLinkOut()
+xout_nn.setStreamName("nn")
+xout_nn.input.setBlocking(False)
+
+detection_nn.out.link(xout_nn.input)
+
+# Pipeline defined, now the device is assigned and pipeline is started
+device = dai.Device(pipeline)
+device.startPipeline()
+
+# Output queues will be used to get the rgb frames and nn data from the outputs defined above
+q_nn_input = device.getOutputQueue(name="nn_input", maxSize=4, blocking=False)
+q_nn = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
+
+start_time = time.time()
+counter = 0
+fps = 0
+layer_info_printed = False
+
+while True:
+    # instead of get (blocking) used tryGet (nonblocking) which will return the available data or None otherwise
+    in_nn_input = q_nn_input.get()
+    in_nn = q_nn.get()
+
+    if in_nn_input is not None:
+        # if the data from the rgb camera is available, transform the 1D data into a HxWxC frame
+        shape = (3, in_nn_input.getHeight(), in_nn_input.getWidth())
+        data = in_nn_input.getData()
+        # TODO: FIx this mess
+        frame = np.array(data).astype(np.uint8).view(np.float16).reshape(shape).transpose(1, 2, 0).astype(np.uint8)
+        cv2.imshow("rgb", frame)
+        # frame = np.ascontiguousarray(frame)
+
+    if in_nn is not None:
+        # print("NN received")
+        layer1 = in_nn.getFirstLayerFp16()
+            # reshape to numpy array
+        lay1 = np.asarray(layer1, dtype=np.float16).reshape((nn_shape, nn_shape))
+
+        # print(lay1)
+
+        newMatrix = np.array(lay1, dtype=np.int32)
+        # print(newMatrix)
+        output_colors = decode_deeplabv3p(newMatrix)
+        if frame is not None:
+            cv2.imshow("selfie", show_deeplabv3p(output_colors, frame, 1.0))
+            frame = show_deeplabv3p(output_colors, frame)
+            cv2.putText(frame, "NN fps: {:.2f}".format(fps), (20,20), cv2.FONT_HERSHEY_TRIPLEX, 0.4, (255, 0, 0))
+            cv2.imshow("nn_input", frame)
+
+    counter+=1
+    if (time.time() - start_time) > 1 :
+        fps = counter / (time.time() - start_time)
+        counter = 0
+        start_time = time.time()
+
+
+    if cv2.waitKey(1) == ord('q'):
+        break
diff --git a/gen2-selfie-segmentation/models/selfie-model.blob b/gen2-selfie-segmentation/models/selfie-model.blob
new file mode 100644
index 00000000..945bed99
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diff --git a/gen2-selfie-segmentation/requirements.txt b/gen2-selfie-segmentation/requirements.txt
new file mode 100644
index 00000000..fbc88b9a
--- /dev/null
+++ b/gen2-selfie-segmentation/requirements.txt
@@ -0,0 +1,2 @@
+opencv-python
+depthai==2.0.0.1