What activation function is used in the following class 123456789class NetRelu(nn.Module):        def __init__(self,D_in,H,D_out):                                     super(NetRelu,self).__init__()                    self.linear1=nn.Linear(D_in,H)                     self.linear2=nn.Linear(H,D_out)                      def forward(self,x):                    x=torch.relu(self.linear1(x)))                      x=self.linear2(x)                 return x 1 pointrelutanh Sigmoid

Question 2What's wrong with the following function :123456789101112 ]:class 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(linear1(x))          x=torch.sigmoid(linear2(x))        return x1 pointyou did not call self.linear1(x) and self .linear2(x)nothing

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)

Consider the following Module or class :123456789101112class Net(nn.Module):    def __init__(self, in_size, n_hidden, out_size, p)        super(Net, self).__init__()        self.drop=nn.Dropout(p=p)        self.linear1=nn.Linear(in_size, n_hidden)        self.linear2=nn.Linear(n_hidden, out_size)    def forward(self, x):        x=torch.relu(self.linear1(x))        x=self.drop(x)        x=self.linear2(x)        return x how would you create a neural network with a dropout parameter of 0.9 1 pointmodel =Net( in_size=10, n_hidden=100, out_size=10, p=0.9)model =Net( in_size=0.9, n_hidden=100, out_size=10, p=10)model =Net( in_size=0.9, n_hidden=0.9, out_size=10, p=10)

Consider the forward function , fill out the value for the if statement marked BLANK . 456789321        for (l, linear_transform) in zip(range(L), self.hidden):            if #BLANK                  activation = torch.relu(linear_transform(activation))            else:                activation = linear_transform(activation)        return activation        L=len(self.hidden)    def forward(self, activation):# Section 2: 1 pointl>Ll > L-1l<L-1

The objective of the Activation Function is to:1 pointReduce the Size of the NetworkHandle Non-Linearity in the NetworkHandle Linearity in the NetworkIncrease the Size of the NetworkNone of the above