class_docstr: Represents a hook to pass to
  :meth:`~TransformerEncoder.register_layer_output_hook`.
class_docstr_tok: ["Represents","a","hook","to","pass","to",":","meth",":","`","~TransformerEncoder.register_layer_output_hook","`","."]
docstr: :param layer_idx:
      The index of the layer in the encoder stack.
  :param layer_output:
      The encoded output of the layer.
  :param layer_padding_mask:
      The padding mask of ``layer_output``.
  :param num_layers:
      The number of layers in the encoder stack.
  
  :returns:
      ``True`` if the encoder should continue executing the remaining
      layers in the stack; ``False`` if the encoder should treat this
      layer as the final layer in the stack.
docstr_tok: [":","param","layer_idx",":","The","index","of","the","layer","in","the","encoder","stack",".",":","param","layer_output",":","The","encoded","output","of","the","layer",".",":","param","layer_padding_mask",":","The","padding","mask","of","``","layer_output","``",".",":","param","num_layers",":","The","number","of","layers","in","the","encoder","stack",".",":","returns",":","``","True","``","if","the","encoder","should","continue","executing","the","remaining","layers","in","the","stack",";","``","False","``","if","the","encoder","should","treat","this","layer","as","the","final","layer","in","the","stack","."]
code: def __call__(
      self,
      layer_idx: int,
      layer_output: Tensor,
      layer_padding_mask: Optional[PaddingMask],
      num_layers: int,
  ) -> bool:
      """
      :param layer_idx:
          The index of the layer in the encoder stack.
      :param layer_output:
          The encoded output of the layer.
      :param layer_padding_mask:
          The padding mask of ``layer_output``.
      :param num_layers:
          The number of layers in the encoder stack.
  
      :returns:
          ``True`` if the encoder should continue executing the remaining
          layers in the stack; ``False`` if the encoder should treat this
          layer as the final layer in the stack.
      """
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, Iterable, Optional, Protocol, Tuple, final
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.hooks import RemovableHandle
  from fairseq2.nn.module_list import ModuleList
  from fairseq2.nn.normalization import LayerNorm
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.nn.transformer.attention_mask import AttentionMaskFactory
  from fairseq2.nn.transformer.encoder_layer import TransformerEncoderLayer
  from fairseq2.nn.transformer.layer_norm import LayerNormFactory, create_standard_layer_norm
  from fairseq2.nn.transformer.norm_order import TransformerNormOrder
  from fairseq2.typing import DataType, Device, finaloverride
label_desc: Use image node_id 1 for calling the EncoderLayerOutputHook obj's underlying member method code with example usage: obj.__call__(layer_idx, layer_output, layer_padding_mask, num_layers) without return types