code: def __init__(self, config):
      super().__init__(config)
  
      self.embed_dim = config.hidden_size
      self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
      self.wpe = nn.Embedding(
          config.max_position_embeddings, self.embed_dim
      )
      self.drop = nn.Dropout(float(config.embed_dropout))
      self.h = nn.ModuleList(
          [
              GPTNeoBlock(config, layer_id=i)
              for i in range(config.num_layers)
          ]
      )
      self.ln_f = nn.LayerNorm(
          self.embed_dim, eps=config.layer_norm_epsilon
      )
  
      self.gradient_checkpointing = False
      # Initialize weights and apply final processing
      self.post_init()
  
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 GPTNeoModel object from inherited base classes: GPTNeoPreTrainedModel with example: obj = GPTNeoModel(config)