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 __init__(self, *modules: nn.Module) -> None:
      super().__init__()
      self.modules_list = nn.ModuleList(modules)
  
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 1 to create a new MultiInputSequential object from inherited base classes: nn with example: obj = MultiInputSequential()