code: def bigcode_config_to_gpt2_config(
      bigcode_config: GPTBigCodeConfig,
  ) -> GPT2Config:
      return GPT2Config(
          activation_function=bigcode_config.activation_function,
          attn_pdrop=bigcode_config.attn_pdrop,
          bos_token_id=bigcode_config.bos_token_id,
          embd_pdrop=bigcode_config.embd_pdrop,
          eos_token_id=bigcode_config.eos_token_id,
          initializer_range=bigcode_config.initializer_range,
          layer_norm_epsilon=bigcode_config.layer_norm_epsilon,
          max_batch_size=bigcode_config.max_batch_size,
          max_sequence_length=bigcode_config.max_sequence_length,
          model_type=bigcode_config.model_type,
          multi_query=bigcode_config.multi_query,
          n_embd=bigcode_config.n_embd,
          n_head=bigcode_config.n_head,
          n_inner=bigcode_config.n_inner,
          n_layer=bigcode_config.n_layer,
          n_positions=bigcode_config.n_positions,
          resid_pdrop=bigcode_config.resid_pdrop,
          scale_attn_weights=bigcode_config.scale_attn_weights,
          summary_activation=bigcode_config.summary_activation,
          summary_first_dropout=bigcode_config.summary_first_dropout,
          summary_proj_to_labels=bigcode_config.summary_proj_to_labels,
          summary_type=bigcode_config.summary_type,
          summary_use_proj=bigcode_config.summary_use_proj,
          use_cache=bigcode_config.use_cache,
          vocab_size=bigcode_config.vocab_size,
      )
  
file_path: flash-attention/flash_attn/models/bigcode.py
imports: import math
  import re
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  from transformers import GPT2Config, GPTBigCodeConfig, PretrainedConfig
label_desc: Use image node_id 3 for calling a global function with example usage: bigcode_config_to_gpt2_config(bigcode_config) and returns: GPT2Config