human
I am working on the file "Equation_Notes2.ipynb".
The first task is:
Forward and Backward Hooks
For inspecting / modifying the output and grad_output of a layer during forward or backward propogation we introduce hooks. 
A hook is a function that can be registered on a module or a tensor.
The hook can be a forward hook or a backward hook. 
The register_forward_hook & register_backward_hook functions are similar to the register_hook variable function, which can be register_hook when the module propagates forward or backpropagates.
A forward hook function is executed each time the forward propagation execution ends. 
The forward-propagating hook function has the following form: 
hook(module, input, output) -> None, 
and backpropagation has the following form: 
hook(module, grad_input, grad_output) -> Tensor or None .
The hook function should not modify the input and output, and should be deleted in time after use to avoid running the hook every time which can increase the running load. 
Hook functions are mainly used in scenarios where some intermediate results are obtained, such as the output of a layer in the middle or the gradient of a layer. These results should have been written in the forward function, but if you add these processing to the forward function, it may make the processing logic more complicated. At this time, it is more appropriate to use the hook technique. 
Let's consider a scenario. There is a pre-trained model that needs to extract the output of a layer (not the last layer) of the model as a feature, but does not want to modify its original model definition file. You can use the hook function. The code of the implementation is given below.
PyTorch recursively applies any hook to all submodules on a register_forward_hook() call.
Can you write Python program for it?
assistant
#Here is the Python program you requested.

#!pip3 install torch torchvision

import torch

import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt

x = torch.randn(1, 2).cuda()
y = torch.randn(1, 1).cuda()

class net(torch.nn.Module):
  def __init__(self):
    super(net,self).__init__()
    self.linear1=torch.nn.Linear(2,1)
    self.linear2=torch.nn.Linear(1,2)
    
  def forward(self,x):
    h_relu = torch.nn.functional.relu(self.linear1(x))
    y_pred = self.linear2(h_relu)
    return y_pred
  
def forwardcall(module,input,output):
  print("\nInside forward of ", module," : ",input," : ", output)
  
def backwardcall(module, grad_input, grad_output):
  print("Inside backward of ", module," : ",grad_input," : ", grad_output)

mymodel = net()

print(mymodel)
hook1 = mymodel.linear1.register_forward_hook(forwardcall)
hook2 = mymodel.linear2.register_backward_hook(backwardcall)

  
if torch.cuda.is_available():
  mymodel.cuda()

optimizer = torch.optim.SGD(mymodel.parameters(), lr=0.0009, momentum=0.9)

loss_col = []
epochs = 5
for epoch in range(epochs):
  optimizer.zero_grad()
  model_output = mymodel(x)
  loss = torch.nn.functional.mse_loss(model_output, y) 
  loss_col.append(loss)
  #print(epoch, "{:.20f}".format(loss.item()))
  loss.backward()
  optimizer.step()
  
hook1.remove()  # removes the hook
hook2.remove()  # removes the hook