I’m trying to understand the lines below:

```
class MyModule(nn.Module):
def __init__(self, in_channels, out channels):
super(MyModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv3_3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=3, dilation=3)
def forward(self, x):
x_y = self.avg_pool(x)
x_c = self.conv3_3(x_y)
return x_c
```

which translates into: `CONV3_DIL3(AVG_POOL1(X))`

. Now:

- Imagine that
`X.shape = [1, 64, 16, 16]`

, i.e.`BxCxHxW`

. - After applying the
`X_Y=AVG_POOL1(X)`

, we obtain`X_Y.shape = [1, 64, 1, 1]`

=>`1xCx1x1`

- Finally, after applying
`X_C= CONV3_DIL3(X_Y)`

we obtain`X_Y.shape = [1, 64, 1, 1]`

=>`1xCx1x1`

because of the padding.

Now, my questions are: (i) what does it exactly mean to apply a 2D Conv with a `3x3`

filter with `dilation=3`

into a `1xCx1x1`

tensor, especially because there’s no spatial information in a flat `1xCx1x1`

tensor (ii) why does this make sense at all?