code: def __init__(self, config, layer_id):
      super().__init__()
      hidden_size = config.hidden_size
      inner_dim = (
          config.intermediate_size
          if config.intermediate_size is not None
          else 4 * hidden_size
      )
      self.ln_1 = nn.LayerNorm(
          hidden_size, eps=config.layer_norm_epsilon
      )
      self.attn = GPTNeoAttention(config, layer_id)
      self.ln_2 = nn.LayerNorm(
          hidden_size, eps=config.layer_norm_epsilon
      )
      self.mlp = GPTNeoMLP(inner_dim, config)
  
imports: import os
  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, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 1 to create a new GPTNeoBlock object from inherited base classes: nn with example: obj = GPTNeoBlock(config, layer_id)