human
Write me a the Python code you requested given the following:
I am working on the file "Equation_Notes2.ipynb".
The first task is:
Using Class
In PyTorch when using classes, we define the Models as subclasses of torch.nn.Module. 
The two functions required while defining (inherenting) any model using class are:

init - We define all the parametric (and sometimes non-parametric) layers. This function has to always be inherited first, then we define parameters of the layer such as the class variables i.e. self.x
forward  - We connect all the layers and other functions to the input to form a graph.

In the _init_ function,  we initialize the layers we want to use and Pytorch goes more low level and we have to specify the sizes of our network so that everything matches.
In the forward method, we specify the connections of your layers. This means that we will use the layers we already initialized, in order to re-use the same layer for each forward pass of data we make.
For layers that do not have trainable weights, we can use either the layer form (from torch.nn) or connection form (from torch.nn.functional),
Can you write Python code for it?
assistant
#Here is the Python code you requested.

#!pip3 install torch torchvision
import torch

import torch.nn as nn
import torch.nn.functional as F

class net(torch.nn.Module):
  def __init__(self):
    super(net,self).__init__() # Can also use torch.nn.Module.__init__(self)
    self.linear1=torch.nn.Linear(D_in,H)
    self.linear2=torch.nn.Linear(H,D_out)
    
  def forward(self,x):
    h_relu = torch.nn.functional.relu(self.linear1(x))
    #h_relu = self.linear1(x).clamp(min=0)
    y_pred = self.linear2(h_relu)
    return y_pred