def forward(self, x):
        # Set initial hidden and cell states
        h0 = torch.zeros(self.num_layers, self.batch_size, self.hidden_size1).to(device)
        c0 = torch.zeros(self.num_layers, self.batch_size, self.hidden_size1).to(device)
#         print(h0)
#         print(x.shape)
#         print(c0)
        #y = np.zeros((self.batch_size, input_size, input_size))
        x = x.view(self.batch_size, x.shape[1], 1)
        hidden = (h0, c0)
        # Forward propagate LSTM

        #print("h0.shape and c0.shape" + str(h0.shape))
        #print("x.shape" + str(x.shape))
        out, _ = self.lstm(x, hidden)  # out: tensor of shape (batch_size, seq_length, hidden_size)
        print("out.shape:")
        print(out.shape)
        # Decode the hidden state of the last time step
        #print("after lstm:" + str(out.shape))
        out = self.dense1(out)
        out = self.tanh1(out)
        #print("after first dense layer:" + str(out.shape))
        out = out.view(batch_size, x.shape[1])
        out = self.dense2(out)
        out = self.tanh2(out)
        #print("after second dense layer:" + str(out.shape))
        #print(out.view(-1).shape)
        out = self.dense3(out)
        out = out.view(batch_size)
        return out