In PyTorch, the ______________ class is used to define a custom neural network architecture.

Question 1Consider the constructor for the following neural network class :1234567class Net(nn.Module):    # Section 1:     def __init__(self, Layers):        super(Net,self).__init__()        self.hidden = nn.ModuleList()        for input_size,output_size in zip(Layers,Layers[1:]):            self.hidden.append(nn.Linear(input_size,output_size))Let us create an object model = Net([2,3,4,4])How many hidden layers are there in this model?

What is a typical use of PyTorch`s torch.nn module?To visualize the training processTo perform advanced mathematical operationsTo create and train neural networksTo perform data preprocessing

Consider the following neural network model or class:1234567891011class Net(nn.Module):    def __init__(self,D_in,H,D_out):        super(Net,self).__init__()        self.linear1=nn.Linear(D_in,H)        self.linear2=nn.Linear(H,D_out)             def forward(self,x):        x=torch.sigmoid(self.linear1(x))          x=torch.sigmoid(self.linear2(x))        return xHow many hidden neurons does the following neural network object have?1model=Net(1,6,1)

How does PyTorch's dynamic computational graph and the Autograd module facilitate the process of building, modifying, and training neural networks compared to other machine learning libraries? Discuss the advantages and any potential challenges you might face. Additionally, provide examples of real-world applications where PyTorch can be particularly beneficial. Instructions:

In TensorFlow, a model is typically built using the ______________ API for simple, layer-by-layer construction.