diff --git a/src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/tensor/TensorBuilder.java b/src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/tensor/TensorBuilder.java
index 9e46072..8fbcdb6 100644
--- a/src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/tensor/TensorBuilder.java
+++ b/src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/tensor/TensorBuilder.java
@@ -141,7 +141,7 @@ private static Tensor<TUint8> buildUByte(
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( new long[tensorShape.length], flatArr, sArr );
+		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
 		ByteDataBuffer dataBuffer = RawDataBufferFactory.create(flatArr, false);
 		Tensor<TUint8> ndarray = Tensor.of(TUint8.DTYPE, Shape.of(ogShape), dataBuffer);
 		return ndarray;
@@ -170,7 +170,7 @@ private static Tensor<TInt32> buildInt(
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( new long[tensorShape.length], flatArr, sArr );
+		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
 		IntDataBuffer dataBuffer = RawDataBufferFactory.create(flatArr, false);
 		Tensor<TInt32> ndarray = TInt32.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
@@ -200,7 +200,7 @@ private static Tensor<TInt64> buildLong(
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( new long[tensorShape.length], flatArr, sArr );
+		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
 		LongDataBuffer dataBuffer = RawDataBufferFactory.create(flatArr, false);
 		Tensor<TInt64> ndarray = TInt64.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
@@ -230,7 +230,7 @@ private static Tensor<TFloat32> buildFloat(
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( new long[tensorShape.length], flatArr, sArr );
+		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
 		FloatDataBuffer dataBuffer = RawDataBufferFactory.create(flatArr, false);
 		Tensor<TFloat32> ndarray = TFloat32.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
@@ -260,7 +260,7 @@ private static Tensor<TFloat64> buildDouble(
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( new long[tensorShape.length], flatArr, sArr );
+		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
 		DoubleDataBuffer dataBuffer = RawDataBufferFactory.create(flatArr, false);
 		Tensor<TFloat64> ndarray = TFloat64.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;