code: def __init__(self, config: Speech2TextConfig):
      super().__init__()
      self.embed_dim = config.d_model
  
      self.self_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[
          config._attn_implementation
      ](
          embed_dim=self.embed_dim,
          num_heads=config.encoder_attention_heads,
          dropout=config.attention_dropout,
          config=config,
      )
      self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
      self.dropout = config.dropout
      self.activation_fn = ACT2FN[config.activation_function]
      self.activation_dropout = config.activation_dropout
      self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
      self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
      self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_speech_to_text import Speech2TextConfig
label_desc: Use image node_id 1 to create a new Speech2TextEncoderLayer object from inherited base classes: nn with example: obj = Speech2TextEncoderLayer(config)