human
Solve the following python programming problem given the following code:
# CODE HERE
class LSTMnetwork(nn.Module):
    
    
    
    
    
    
    
    


# Run the code below to check your results:
torch.manual_seed(101)
model = LSTMnetwork()
model

# DON'T WRITE HERE
class LSTMnetwork(nn.Module):
    def __init__(self,input_size=1,hidden_size=64,output_size=1):
        super().__init__()
        self.hidden_size = hidden_size
        
        # Add an LSTM layer:
        self.lstm = nn.LSTM(input_size,hidden_size)
        
        # Add a fully-connected layer:
        self.linear = nn.Linear(hidden_size,output_size)
        
        # Initialize h0 and c0:
        self.hidden = (torch.zeros(1,1,self.hidden_size),
                       torch.zeros(1,1,self.hidden_size))

    def forward(self,seq):
        lstm_out, self.hidden = self.lstm(
            seq.view(len(seq),1,-1), self.hidden)
        pred = self.linear(lstm_out.view(len(seq),-1))
        return pred[-1]
    
torch.manual_seed(101)
model = LSTMnetwork()
model
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

4. Define the model
Design a model that has a (1,64) LSTM layer and a (64,1) fully-connected linear layer. Be sure to initialize $h_0$ and $c_0$, and return only the last predicted value.