class_docstr: Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Speech2Text2DecoderLayer`]
  
  Args:
      config: Speech2Text2Config
      embed_tokens (nn.Embedding): output embedding
class_docstr_tok: ["Transformer","decoder","consisting","of","*","config.decoder_layers","*","layers",".","Each","layer","is","a","[","`","Speech2Text2DecoderLayer","`","]","Args",":","config",":","Speech2Text2Config","embed_tokens","(","nn.Embedding",")",":","output","embedding"]
code: def set_input_embeddings(self, value):
      self.embed_tokens = value
  
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_speech_to_text_2 import Speech2Text2Config
label_desc: Use image node_id 3 for calling the Speech2Text2Decoder obj's underlying member method code with example usage: obj.set_input_embeddings(value) without return types