human
I am working on the file "Equation_Notes2.ipynb".
The first task is:
Notice that we never explicitly call forward pass defined within the class. We always call the Module instance afterwards instead since it takes care of running the registered hooks while the former silently ignores them.
We can also obtain the number of trainable parameters of a model using in the last line of code.
torch.nn only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, nn.Conv2d will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use input.unsqueeze(0) to add a fake batch dimension.
This is motivated by the computational speed-up it induces. To evaluate a module on a sample, both the module’s parameters and the sample have to be first copied into cache memory, which is fast but small. For any model of reasonable size, only a fraction of its parameters can be kept in cache, so a module’s parameter have to be copied there every time it is used. These memory transfers are slower than the computation itself.This is the main reason for batch processing: it cuts down to one per module per batch the number of copies of parameters to the cache. It also cuts down the use of Python loops, which are awfully slow.
torch.nn vs torch.nn.functional
A nn.Module is actually a OO wrapper around the functional interface, that contains a number of utility methods, like eval() and parameters(), and it automatically creates the parameters of the modules for us. 
We can use the functional interface whenever we want, but that requires us to define the weights by hand. 
The difference between torch.nn and torch.nn.functional is very subtle. In fact, many torch.nn.functional have a corresponding equivalent in torch.nn. 
In many code samples, we use torch.nn.functional for simpler operations that have no trainable parameters or configurable parameters. For example for ReLU, we do not require any learnable parameters to be called in forward() method hence it can be defined using the...
Can you write Python code for it?

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Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.

import torch

class Model(torch.nn.Module):
    super().__init__()
    self.feature_extractor = nn.Sequential(
        Conv2d(3, 12, kernel_size=3, padding=1, stride=1),
        Conv2d(12, 24, kernel_size=3, padding=1, stride=1),)

    def forward(self, x):
        x = self.feature_extractor(x)
        return x