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 __init__(self, config):
      super().__init__(config)
      self.decoder = Speech2Text2Decoder(config)
  
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_speech_to_text_2 import Speech2Text2Config
label_desc: Use image node_id 1 to create a new Speech2Text2DecoderWrapper object from inherited base classes: Speech2Text2PreTrainedModel with example: obj = Speech2Text2DecoderWrapper(config)