Hello, I am having issues during the `loss.backward()`

step in my implementations. Specifically, a submodule of my implementation is essentially an L2 distance calculation of my input (of size 128x46x46) with a specific set of vectors. The result is the concatenated tensor of these distances. The code is presented below:

```
class CentroidDistances(nn.Module):
"""Class that extends the Module class to create a custom layer for our final model"""
def __init__(self, centroids):
super(CentroidDistances, self).__init__()
self.centroids = centroids
def forward(self, x):
distance_list = []
for i in self.centroids:
distance_list.append(torch.sum(torch.pow(x.sub(i.expand_as(x)), 2), 1))
result = torch.cat(distance_list, dim=0)
return result
```

The problem is that during the `backward`

step, this code produces an Error:

RuntimeError: size ‘[1 x 46 x 46]’ is invalid for input of with 270848 elements

Does anyone have an idea of how to circumvent this problem? Because according to other topics, the `torch.cat()`

function can be backpropagated. Thank you!