code: def merge_partition(
      model,
      partitions: Dict[int, List[List[torch.Tensor]]],
      write_path: str = None,
  ):
      # Extract the pp_rank and number of modules per tp rank in each pp rank
      pp_ranks = list(partitions.keys())
      pp_lens = []
      for pp_rank in pp_ranks:
          partition_pp = partitions[pp_rank]
          max_len = max(
              [len(x) for x in partition_pp]
          )  # Perform max as we need to iterate through all modules
          pp_lens.append(max_len)
  
      total_params_merged = len([p for p in model.parameters()])
      pp_total_len = sum(pp_lens)
      logging.info(
          f"Total layers in Merged Model: {total_params_merged}"
      )
  
      og_pp_split_rank = 0
      if pp_total_len > total_params_merged:
          og_pp_split_rank = model.cfg.get(
              "pipeline_model_parallel_split_rank", 0
          )
  
      idx = 0
      pp_rank = 0
      global_idx = 0
  
      # During merge - model is TP 1 PP 1 model with all parameters present in correct order.
      # Merge the parameters of the various PP X TP Y models into the TP 1 PP 1 model.
      for name, param in model.named_parameters():
          # Since the PP ranks each contain the list of all their TP rank parameters
          # We need to detect if we need to move to the next PP rank when we run out of tensors in current PP rank
          # Reset the index so that it indexes the new pp rank tensor list correctly
          if idx >= pp_lens[pp_rank]:
              pp_rank += 1
              idx = 0
  
              # For EncDec models, after the encoder-decoder PP split occurs,
              # the vocab and positional embeddings are duplicated across the PP ranks at the
              # beginning of the decoder rank. We can skip them during the merge step.
              if pp_total_len > total_params_merged:
                  if (
                      og_pp_split_rank > 0
                      and og_pp_split_rank == pp_rank
                  ):
                      logging.info(
                          f"Skipping duplicate vocab and positional embeddings for EncDec model "
                          f"at the pp split rank: {og_pp_split_rank}"
                      )
                      idx += 2
  
          # For EncDec models, after the pp split occurs, final pp rank of the decoder
          # has an intermediate embedding tensor at the penultimate positon, skip that.
          if (
              og_pp_split_rank > 0
              and global_idx == total_params_merged - 1
          ):
              logging.info(
                  f"Skipping intermediate embedding tensor for EncDec model at the final pp split "
                  f"rank: {og_pp_split_rank}",
              )
              idx = pp_lens[pp_rank] - 1
  
          # Extract all TP ranks out of current PP rank
          partitions_pp = partitions[pp_rank]
  
          logging.debug(
              f"Global idx: {global_idx} Index: {idx} Model Param: {name} "
              f"Partition Params: {[p[idx].shape for p in partitions_pp]}"
          )
  
          # Original TP rank change logic
          concated = compute_tp_merge(
              idx, name, param, partitions_pp, model.cfg
          )
  
          # Update the model parameter with the merged tensor
          param.data = concated
          idx += 1
          global_idx += 1
  
      # Save the file iff the original file was PP 1 TP 1
      if write_path is not None:
          model.save_to(write_path)
  
imports: import os
  import shutil
  import tarfile
  import tempfile
  from argparse import ArgumentParser
  from typing import Dict, List
  import torch
  import torch.nn
  from omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.nlp.parts.nlp_overrides import NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPSaveRestoreConnector, PipelineMixedPrecisionPlugin
  from nemo.utils import logging, model_utils
  from nemo.utils.app_state import AppState
label_desc: Use image node_id 8 for calling a global function with example usage: merge_partition(model, partitions, write_path) without return types