code: def __init__(self, config):
      super().__init__()
      inner_dim = (
          config.n_inner
          if config.n_inner is not None
          else 4 * config.n_embd
      )
      self.ln_1 = nn.LayerNorm(
          config.n_embd, eps=config.layer_norm_epsilon
      )
      self.attn = GPTJAttention(config)
      self.mlp = GPTJMLP(inner_dim, config)
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.fx
  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, is_torch_fx_proxy, 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 GPTJBlock object from inherited base classes: nn with example: obj = GPTJBlock(config)