code: def __init__(self, config: TrOCRConfig):
      super().__init__()
      self.embed_dim = config.hidden_size
  
      self.self_attn = TrOCRAttention(
          config,
          embed_dim=self.embed_dim,
          num_heads=config.decoder_attention_heads,
          dropout=config.attention_dropout,
          is_decoder=True,
      )
      self.dropout = config.dropout
      self.activation_fn = ACT2FN[config.activation_function]
      self.activation_dropout = config.activation_dropout
  
      self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
  
      if config.is_decoder:
          self.encoder_attn = TrOCRAttention(
              config,
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              kdim=config.cross_attention_hidden_size,
              vdim=config.cross_attention_hidden_size,
              dropout=config.attention_dropout,
              is_decoder=True,
              is_cross_attention=True,
          )
          self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
  
      self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
      self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
      self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
imports: import copy
  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 BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, logging, replace_return_docstrings
  from .configuration_trocr import TrOCRConfig
label_desc: Use image node_id 1 to create a new TrOCRDecoderLayer object from inherited base classes: nn with example: obj = TrOCRDecoderLayer(config)