code: def convert_state_dict(
      state_dict: Dict[str, torch.Tensor], amp: bool = False
  ):
      def get_new_key(old_key):
          if old_key == "transformer.word_embeddings.weight":
              return "embedding.word_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")
  
          # For the rest, a base transformation
          key = old_key.replace("transformer.h", "decoder.layers")
  
          # Handling the layer normalization replacements
          if falcon_config.new_decoder_architecture:
              key = key.replace("ln_attn", "input_layernorm")
              key = key.replace("ln_mlp", "pre_mlp_layernorm")
          else:
              key = key.replace("input_layernorm", "input_layernorm")
              if not falcon_config.parallel_attn:
                  key = key.replace(
                      "post_attention_layernorm",
                      "post_self_attn_layernorm",
                  )
  
          key = key.replace(
              "self_attention.dense", "self_attention.linear_proj"
          )
          key = key.replace(
              "self_attention.query_key_value",
              "self_attention.linear_qkv",
          )
          key = key.replace("dense_h_to_4h", "linear_fc1")
          key = key.replace("dense_4h_to_h", "linear_fc2")
          return key
  
      new_dict = {}
      # amp O2 mode has different state dict name
      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_hf_falcon_to_nemo.py
imports: import argparse
  import time
  from typing import Dict
  import pytorch_lightning
  import torch
  import yaml
  from omegaconf import OmegaConf
  from transformers import AutoModelForCausalLM, FalconConfig
  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, key, str, old_key, old_key