class_docstr: Implements a single Transformer decoder layer without attending to the encoder
  states.
  
  Decoder layers are similar to encoder layers but self-attention is limited in a
  causal (auto-regressive) manner.
docstr: Overridden to allow swapping out of the feedforward class at instantiation.
docstr_tok: ["Overridden","to","allow","swapping","out","of","the","feedforward","class","at","instantiation","."]
code: def build_feedforward(
      self,
      dim: int = None,
      dim_hidden: int = None,
      relu_dropout: float = 0,
      activation: str = "relu",
  ) -> TransformerFFN:
      """
      Overridden to allow swapping out of the feedforward class at instantiation.
      """
      return self.swappables.feedforward(  # type: ignore
          opt=self.opt,
          dim=dim,
          dim_hidden=dim_hidden,
          relu_dropout=relu_dropout,
          activation=activation,
      )
  
imports: from __future__ import annotations
  from abc import ABC
  from typing import Dict, Optional, Tuple
  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.core.torch_agent import DictionaryAgent
  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 2 for calling the TransformerDecoderOnlyLayer obj's underlying member method code with example usage: obj.build_feedforward(dim, dim_hidden, relu_dropout, activation) and returns: self