Hello

I would like to do learning weights of a convolution but using torch.nn.functional.conv2d instead of torch.nn.conv2d as in this tutorial: https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html , because the weights tensor when using torch.nn.conv2d has the shape (C_out, C_in, H_in, H_w) and I rather want (C_out, C_in, 1,1) in my model.

```
%matplotlib inline
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# self.w_i = torch.ones(10, 3, 1, 1, requires_grad=True)
self.w_i = nn.Parameter(torch.FloatTensor(3,3,1,1))
def forward(self, x):
x = F.conv2d(x, self.w_i)
return x
net = Net()
print(net)
input = torch.ones(1, 3, 5, 5)
true_weights = torch.Tensor([[[[ 2.]],[[ 3.]],[[ 1.]]],
[[[ 0.]],[[ 5.]],[[ 2.]]],
[[[ 1.]],[[ 1.]],[[ 2.]]]])
target = F.conv2d(input,true_weights)
criterion = nn.MSELoss()
import torch.optim as optim
optimizer = optim.SGD(net.parameters(), lr=0.01)
for epoch in range(10000):
optimizer.zero_grad() # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step() # Does the update
list(net.parameters())
```

I am new on Pytorch and the code above returns me weights in net.parameters() that is not correct (I want true_weights).

What should I modify to make the code run ?