human
Here happens after a forward pass through this network! given the following:
I am working on the file "18-11-22-Deep-Learning-with-PyTorch/03-Convolutional Neural Networks/Part 5 - Convolution visualisation.ipynb".
The first task is:
Define a convolutional layer
The various layers that make up any neural network are documented, here. For a convolutional neural network, we'll start by defining a:
* Convolutional layer
Initialize a single convolutional layer so that it contains all your created filters. Note that you are not training this network.
you are initializing the weights in a convolutional layer so that you can visualize what happens after a forward pass through this network!
__init__ and forward
To define a neural network in PyTorch, you define the layers of a model in the function __init__ and define the forward behavior of a network that applyies those initialized layers to an input (x) in the function forward. In PyTorch we convert all inputs into the Tensor datatype, which is similar to a list data type in Python. 
Below, I define the structure of a class called Net that has a convolutional layer that can contain four 3x3 grayscale filters.
Can you write Python code for it?
assistant
#Here happens after a forward pass through this network!
.

import torch
import torch.nn as nn
import torch.nn.functional as F
    
# define a neural network with a single convolutional layer with four filters
class Net(nn.Module):
    
    def __init__(self, weight):
        super(Net, self).__init__()
        # initializes the weights of the convolutional layer to be the weights of the 4 defined filters
        k_height, k_width = weight.shape[2:]
        # assumes there are 4 grayscale filters
        self.conv = nn.Conv2d(1, 4, kernel_size=(k_height, k_width), bias=False)
        self.conv.weight = torch.nn.Parameter(weight)

    def forward(self, x):
        # calculates the output of a convolutional layer
        # pre- and post-activation
        conv_x = self.conv(x)
        activated_x = F.relu(conv_x)
        
        # returns both layers
        return conv_x, activated_x
    
# instantiate the model and set the weights
weight = torch.from_numpy(filters).unsqueeze(1).type(torch.FloatTensor)
model = Net(weight)

# print out the layer in the network
print(model)