error with model FashionMnist need your help

[ShreshShaurya]

Create a convolutional neural network

class FashionMNISTModelV2(nn.Module):
"""
Model architecture copying TinyVGG from:
https://poloclub.github.io/cnn-explainer/
"""
def init(self, input_shape: int, hidden_units: int, output_shape: int):
super().init()
self.block_1 = nn.Sequential(
nn.Conv2d(in_channels=input_shape,
out_channels=hidden_units,
kernel_size=3, # how big is the square that's going over the image?
stride=1, # default
padding=1),# options = "valid" (no padding) or "same" (output has same shape as input) or int for specific number
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2,
stride=2) # default stride value is same as kernel_size
)
self.block_2 = nn.Sequential(
nn.Conv2d(hidden_units, hidden_units, 3, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_units, hidden_units, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.classifier = nn.Sequential(
nn.Flatten(),
# Where did this in_features shape come from?
# It's because each layer of our network compresses and changes the shape of our input data.
nn.Linear(in_features=hidden_units77, #used a trick to calculate this
out_features=output_shape)
)

def forward(self, x: torch.Tensor):
    x = self.block_1(x)
    # print(x.shape)
    x = self.block_2(x)
    # print(x.shape)
    x = self.classifier(x)
    # print(x.shape)
    return x

What I don't understand is how the accuracy in eval is worse but during test and train is ok. I am not able to pinpoint the error. Would love to know where I am doing wrong.

Also, it is just detecting pullover, I feel like I messed up the data somewhere but I don't understand how.