class_docstr: Sequential which can take more than 1 argument in forward function
class_docstr_tok: ["Sequential","which","can","take","more","than","1","argument","in","forward","function"]
code: def forward(self, *inputs):
      for module in self._modules.values():
          if type(inputs) == tuple:
              inputs = module(*inputs)
          else:
              inputs = module(inputs)
      return inputs
  
imports: import logging
  import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import parameters, types
  from reagent.models import convolutional_network, fully_connected_network
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP
label_desc: Use image node_id 1 for calling the SequentialMultiArguments obj's underlying member method code with example usage: obj.forward() and returns: inputs