class_docstr: Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
  
  Args:
      config: OPTConfig
class_docstr_tok: ["Transformer","decoder","consisting","of","*","config.num_hidden_layers","*","layers",".","Each","layer","is","a","[","`","OPTDecoderLayer","`","]","Args",":","config",":","OPTConfig"]
code: def set_input_embeddings(self, value):
      self.embed_tokens = value
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.opt.configuration_opt import OPTConfig
label_desc: Use image node_id 3 for calling the OPTDecoder obj's underlying member method code with example usage: obj.set_input_embeddings(value) without return types