code: def __init__(self, config):
      config = copy.deepcopy(config)
      config.is_decoder = True
      config.is_encoder_decoder = False
      super().__init__(config)
      self.model = TrOCRDecoderWrapper(config)
  
      self.output_projection = nn.Linear(
          config.hidden_size, config.vocab_size, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
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 TrOCRForCausalLM object from inherited base classes: TrOCRPreTrainedModel with example: obj = TrOCRForCausalLM(config)