code: def __init__(self, config):
      super().__init__(config)
  
      self.d_model_size = config.n_embd
      self.num_layers = config.n_layer
  
      self.pos_encoding = positional_encoding(
          config.n_positions, self.d_model_size, torch.float
      )
  
      self.w = nn.Embedding(config.vocab_size, config.n_embd)
  
      self.dropout = nn.Dropout(config.embd_pdrop)
      self.h = nn.ModuleList(
          [
              EncoderLayer(
                  config.n_embd,
                  config.n_head,
                  config.dff,
                  config.resid_pdrop,
              )
              for _ in range(config.n_layer)
          ]
      )
      self.layernorm = nn.LayerNorm(
          config.n_embd, eps=config.layer_norm_epsilon
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 1 to create a new CTRLModel object from inherited base classes: CTRLPreTrainedModel with example: obj = CTRLModel(config)