Runtime error after trained using anchor box transform

RuntimeError Traceback (most recent call last)
Cell In[91], line 2
1 # Call the function with the modified parameters
----> 2 postprocess_and_export_predictions(model, test_given_loader, device, predictions, use_dems, postprocess=True, threshold=0)

Cell In[81], line 29, in postprocess_and_export_predictions(model, test_given_loader, device, predictions_path, use_dems, postprocess, threshold, p_threshold, all_means, all_stds)
26 data_batch = data_batch.to(device)
28 # Predict and apply a threshold for binary predictions
—> 29 outputs = model(data_batch)
30 predictions = torch.sigmoid(outputs) > p_threshold
32 for idx, prediction in enumerate(predictions):
33 # Verification based on the red band

File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
→ 1518 return self._call_impl(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
1522 # If we don’t have any hooks, we want to skip the rest of the logic in
1523 # this function, and just call forward.
1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
→ 1527 return forward_call(*args, **kwargs)
1529 try:
1530 result = None

Cell In[68], line 67, in UNetModified.forward(self, input)
64 x4_0 = self.conv4_0(self.pool(x3_0))
66 # Use the pad_and_concat function for each concatenation step
—> 67 x3_1 = self.conv3_1(pad_and_concat(self.up(x4_0), x3_0))
68 x2_2 = self.conv2_2(pad_and_concat(self.up(x3_1), x2_0))
69 x1_3 = self.conv1_3(pad_and_concat(self.up(x2_2), x1_0))

Cell In[68], line 32, in pad_and_concat(x1, x2)
28 x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
29 diffY // 2, diffY - diffY // 2])
31 # Concatenating along the channel dimension
—> 32 return torch.cat([x2, x1], dim=1)

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 43 but got size 44 for tensor number 1 in the list.