class_docstr: This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
  used in combination with the [`EncoderDecoderModel`] framework.
code: def forward(self, *args, **kwargs):
      return self.decoder(*args, **kwargs)
  
imports: import copy
  import math
  import random
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  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 2 for calling the TrOCRDecoderWrapper obj's underlying member method code with example usage: obj.forward() and returns: self