code: def convert_state_dict(
      state_dict: Dict[str, torch.Tensor], amp: bool = False
  ):
      def get_new_key(old_key):
          if old_key == "transformer.wte.weight":
              return "embedding.word_embeddings.weight"
          if old_key == "transformer.wpe.weight":
              return "embedding.position_embeddings.weight"
          elif old_key.startswith("transformer.ln_f"):
              return old_key.replace(
                  "transformer.ln_f", "decoder.final_layernorm"
              )
          elif old_key.startswith("lm_head"):
              return old_key.replace("lm_head", "output_layer")
          else:
              p1 = old_key.replace("transformer.h", "decoder.layers")
              p2 = p1.replace(
                  "ln_1.", "self_attention.linear_qkv.layer_norm_"
              )
              p3 = p2.replace(
                  "attn.c_proj", "self_attention.linear_proj"
              )
              p4 = p3.replace(
                  "attn.c_attn", "self_attention.linear_qkv"
              )
              p5 = p4.replace("ln_2.", "mlp.linear_fc1.layer_norm_")
              p6 = p5.replace("c_fc", "linear_fc1")
              p7 = p6.replace("c_proj", "linear_fc2")
              return p7
  
      new_dict = {}
      prefix = "model.module." if amp else "model."
  
      for old_key, val in state_dict.items():
          new_key = get_new_key(old_key)
          new_key = prefix + new_key
          new_dict[new_key] = val
  
      return new_dict
  
file_path: NeMo/scripts/nlp_language_modeling/convert_starcoder_hf_to_nemo.py
imports: import argparse
  import os
  from typing import Dict
  import pytorch_lightning
  import torch
  import yaml
  from omegaconf import OmegaConf
  from transformers import AutoConfig, AutoModelForCausalLM
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_state_dict(state_dict, amp) and returns: new_dict, str, str, old_key, old_key, p7