human
I am working on the file "Copy_of_Introduction_to_CNNs_Handout.ipynb".
The first task is:
Implementing Sofmax Regression
Our first model of the day is going to be softmax regression. This model can be summed up as simultaneously calculating the probablity that an image is any of the 10 classes at the same time using a set of parameters that represent the importance of particular pixels in the image. 
We use the function $P(y' = {1..10}) = \sigma(w^Tx + b)$ where $\sigma = \frac{e^{ \boldsymbol x}} {\sum_\limits{i \in dim \boldsymbol x} e^{\boldsymbol x_i}}$ to calculate the probablity and we use gradient decent to find the weights. 
Defining the Model
Pytorch has a super easy way to define machine learning models. We create a sub class of the pytorch class torch.nn.Module listing out the layers of our model in our __init__ method and the operations between those layers in the method called forward. The torch.nn.Module super class handles the implementation of the backpropogation.
Go a head and try to fill in what you think a SoftmaxRegression Model would look like.
Here are some useful functions:

torch.nn.Linear -> Fully Connected Layer / Impliments $F(\boldsymbol x) = \boldsymbol w^T \boldsymbol x + \boldsymbol b$
torch.nn.Functional.softmax -> Softmax function / Implements $F(x) = \frac{e^{ \boldsymbol x}} {\sum_\limits{i \in dim \boldsymbol x} e^{\boldsymbol x_i} }$
torch.nn.Functional.log_softmax -> Log of the softmax function / Implements $F(x) = log(\frac{e^{ \boldsymbol x}} {\sum_\limits{i \in dim \boldsymbol x} e^{\boldsymbol x_i} })$
torch.Tensor.view -> allows you to reshape a Tensor (multi-dimentional vector)
Can you write Python program for it?
assistant
#Here is Python program to find the weights.

class SoftmaxRegression(nn.Module):
    def __init__(self):
        super(SoftmaxRegression, self).__init__()
        '''
        INSERT YOUR MODEL COMPONENTS HERE
        ''' 
    def forward(self, x):
        '''
        LINK YOUR COMPONENTS HERE 
        '''
        return x