code: def forward(self, input, future=0):
      outputs = []
      h_t = dummy_torch_tensor((input.size(0), 51))
      c_t = dummy_torch_tensor((input.size(0), 51))
      h_t2 = dummy_torch_tensor((input.size(0), 51))
      c_t2 = dummy_torch_tensor((input.size(0), 51))
  
      for _, input_t in enumerate(input.chunk(input.size(1), dim=1)):
          h_t, c_t = self.lstm1(input_t, (h_t, c_t))
          h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
          output = self.linear(h_t2)
          outputs += [output]
      for _ in range(future):  # if we should predict the future
          h_t, c_t = self.lstm1(output, (h_t, c_t))
          h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
          output = self.linear(h_t2)
          outputs += [output]
      outputs = torch.stack(outputs, 1).squeeze(2)
      return outputs
  
imports: import pytest
  import wandb
label_desc: Use image node_id 2 for calling the Sequence obj's underlying member method code with example usage: obj.forward(input, future) and returns: outputs