class_docstr: Implements a single Transformer decoder layer with cross (encoder) attention as in.
  
  [Vaswani, 2017](https://arxiv.org/abs/1706.03762).
  
  Decoder layers are similar to encoder layers but:
  
  1. Self-attention is limited in a causal (auto-regressive) manner.
  2. Attend over all of the encoder states.
code: def __init__(
      self,
      opt: Opt,
      n_heads: int = None,
      embedding_size: int = None,
      ffn_size: int = None,
      attention_dropout: float = 0.0,
      relu_dropout: float = 0.0,
      dropout: float = 0.0,
      activation: str = "relu",
      variant: str = "aiayn",
      **kwargs,
  ):
      super().__init__(
          opt=opt,
          n_heads=n_heads,
          embedding_size=embedding_size,
          ffn_size=ffn_size,
          attention_dropout=attention_dropout,
          relu_dropout=relu_dropout,
          dropout=dropout,
          activation=activation,
          variant=variant,
          **kwargs,
      )
  
      n_heads = default(n_heads, opt["n_heads"])
      embedding_size = default(embedding_size, opt["embedding_size"])
  
      self.encoder_attention = self.swappables.encoder_attention(  # type: ignore
          opt=self.opt,
          n_heads=n_heads,
          dim=embedding_size,
          dropout=attention_dropout,
      )
      self.norm2 = torch.nn.LayerNorm(
          embedding_size, eps=LAYER_NORM_EPS
      )
  
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 1 to create a new TransformerDecoderLayer object from inherited base classes: BaseTransformerDecoderLayer with example: obj = TransformerDecoderLayer(opt, n_heads, embedding_size, ffn_size, attention_dropout, relu_dropout, dropout, activation, variant)