class_docstr: Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`]
  
  Args:
      config: WhisperConfig
class_docstr_tok: ["Transformer","decoder","consisting","of","*","config.decoder_layers","*","layers",".","Each","layer","is","a","[","`","WhisperDecoderLayer","`","]","Args",":","config",":","WhisperConfig"]
code: def set_input_embeddings(self, value):
      self.embed_tokens = value
  
imports: import math
  import random
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_whisper import WhisperConfig
label_desc: Use image node_id 3 for calling the WhisperDecoder obj's underlying member method code with example usage: obj.set_input_embeddings(value) without return types