class_docstr: A variation of nn.Sequential, that allows the first module in the sequence accepts
  multiple inputs. To be used internally by _split_module
code: def forward(self, *inputs: Tuple[Tensor]) -> Tensor:  # type: ignore
      input = self.modules_list[0](*inputs)
      for module in self.modules_list[1:]:
          input = module(input)
      return input
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Set, Tuple, Union
  from torch import Tensor, nn
  from torch.distributed import rpc
  from torch.distributed.nn import RemoteModule
  from .data import DataConsumer
label_desc: Use image node_id 2 for calling the MultiInputSequential obj's underlying member method code with example usage: obj.forward() and returns: input