code: def gptj_config_to_gpt2_config(gptj_config: GPTJConfig) -> GPT2Config:
      headdim = gptj_config.n_embd // gptj_config.n_head
      return GPT2Config(
          vocab_size=gptj_config.vocab_size,
          n_positions=0,  # No absolute position embedding
          n_embd=gptj_config.n_embd,
          n_layer=gptj_config.n_layer,
          n_head=gptj_config.n_head,
          n_inner=gptj_config.n_inner,
          activation_function=gptj_config.activation_function,
          resid_pdrop=gptj_config.resid_pdrop,
          embd_pdrop=gptj_config.embd_pdrop,
          attn_pdrop=gptj_config.attn_pdrop,
          layer_norm_epsilon=gptj_config.layer_norm_epsilon,
          initializer_range=gptj_config.initializer_range,
          bos_token_id=gptj_config.bos_token_id,
          eos_token_id=gptj_config.eos_token_id,
          # These are new arguments not in the original GPT2Config
          prenorm=True,
          parallel_block=True,
          parallel_block_tied_norm=True,
          rotary_emb_fraction=gptj_config.rotary_dim / headdim,
          rotary_emb_interleaved=True,
          tie_word_embeddings=False,
          qkv_proj_bias=False,
          out_proj_bias=False,
          lm_head_bias=True,
      )
  
file_path: flash-attention/flash_attn/models/gptj.py
imports: import math
  import re
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  from transformers import GPT2Config, GPTJConfig
label_desc: Use image node_id 2 for calling a global function with example usage: gptj_config_to_gpt2_config(gptj_config) and returns: GPT2Config