human
Write me a the Python program you requested given the following:
I am working on the file "Equation_Notes2.ipynb".
The first task is:
The special method forward() will automatically get called when the layer is calculated and its gonna get passed the data from previous layer.
Using Custom Class
Can you write Python program for it?
assistant
#Here is the Python program you requested.

# -*- coding: utf-8 -*-
import torch

class TwoLayerNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        """
        In the constructor we instantiate two nn.Linear modules and assign them as
        member variables.
        """
        super(TwoLayerNet, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, H)
        self.linear2 = torch.nn.Linear(H, D_out)

    def forward(self, x):
        """
        In the forward function we accept a Tensor of input data and we must return
        a Tensor of output data. We can use Modules defined in the constructor as
        well as arbitrary operators on Tensors.
        """
        #h_relu = self.linear1(x).clamp(min=0)
        h_relu = torch.nn.functional.relu(self.linear1(x))
        y_pred = self.linear2(h_relu)
        return y_pred

# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10

# Create random Tensors to hold inputs and outputs
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

# Construct our model by instantiating the class defined above
model = TwoLayerNet(D_in, H, D_out)

# Construct our loss function and an Optimizer. The call to model.parameters()
# in the SGD constructor will contain the learnable parameters of the two
# nn.Linear modules which are members of the model.
criterion = torch.nn.MSELoss(reduction='sum')

optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)

for t in range(5):
    # Forward pass: Compute predicted y by passing x to the model
    y_pred = model(x) #Construct the graph and return the output of forward

    # Compute and print loss, can directly call nn.functional.mse_loss(out, y)since it is a non-parametric function
    loss = criterion(y_pred, y)
    print(t, "{:.20f}".format(loss.item()))

    # Zero gradients, perform a backward pass, and update the weights.
    optimizer.zero_grad()
    loss.backward()
    
    optimizer.step()
    
sum([p.numel() for p in model.parameters()])