class_docstr: No-op encoder.
  
  Useful for decoder-only transformers.
class_docstr_tok: ["No-op","encoder",".","Useful","for","decoder-only","transformers","."]
docstr: Dummy __init__ to avoid passing superfluous args to nn.Module.
docstr_tok: ["Dummy","__init__","to","avoid","passing","superfluous","args","to","nn.Module","."]
code: def __init__(self, *args, **kwargs):
      """
      Dummy __init__ to avoid passing superfluous args to nn.Module.
      """
      super().__init__()
  
imports: from __future__ import annotations
  from typing import Tuple, Optional, Union
  import numpy
  import torch
  import torch.nn
  from parlai.agents.transformer.modules import create_position_codes, get_n_positions_from_options, LAYER_NORM_EPS, MultiHeadAttention, TransformerFFN
  from parlai.agents.transformer.modules.modular import swappable
  from parlai.core.opt import Opt
  from parlai.core.params import default
  from parlai.utils.misc import warn_once
  from parlai.utils.torch import PipelineHelper
  from parlai.utils.fsdp import fsdp_wrap
  from parlai.nn.checkpoint import checkpoint_wrapper
label_desc: Use image node_id 1 to create a new PassThroughEncoder object from inherited base classes: torch.nn with example: obj = PassThroughEncoder()