code: def __init__(self, config):
      super().__init__(config)
  
      self.embed_dim = config.n_embd
      self.vocab_size = config.vocab_size
      self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
      self.drop = nn.Dropout(config.embd_pdrop)
      self.h = nn.ModuleList(
          [GPTJBlock(config) for _ in range(config.n_layer)]
      )
      self.ln_f = nn.LayerNorm(
          self.embed_dim, eps=config.layer_norm_epsilon
      )
  
      # Model parallel
      self.model_parallel = False
      self.device_map = None
      self.gradient_checkpointing = False
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from ...utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 1 to create a new GPTJModel object from inherited base classes: GPTJPreTrainedModel with example: obj = GPTJModel(config)