code: def compute_tp_merge(idx, name, param, partitions_pp, model_cfg):
      """
      Function to compute the partition merge required for tensor-parallelism.
  
      Args:
          idx:  The index used to select the parameter in the current pipeline partition.
          name:
          param: The parameter to be merged under TP 1 PP 1.
          partitions_pp: List of all TP partitions of the flattened parameter of the current model for a given PP rank
              (TP X PP Y). Indexed as partitions_pp[tp_rank][idx].
          model_cfg: The model config as an OmegaConf DictConfig.
  
      Returns:
          The concatenated parameter for TP 1 PP 1.
      """
      fast_glu_activation = str(
          model_cfg.get("activation", "")
      ).lower() in ["fast-geglu", "fast-swiglu", "fast-reglu"]
  
      # Logic from original TP rank change
      if param.shape == partitions_pp[0][idx].shape:
          concated = partitions_pp[0][idx].data
      elif param.shape[0] == partitions_pp[0][idx].shape[0]:
          concated = torch.cat(
              [
                  partitions_pp[i][idx].data
                  for i in range(len(partitions_pp))
              ],
              dim=-1,
          )
      else:
          concated = torch.cat(
              [
                  partitions_pp[i][idx].data
                  for i in range(len(partitions_pp))
              ],
              dim=0,
          )
  
      # Logic for Fast Glu activation
      if "dense_h_to_4h" in name and fast_glu_activation:
          # concat all the first halves ('W's) and all the second halves ('V's)
          wk_splits = []
          for tpr in range(len(partitions_pp)):
              wk_splits.append(
                  torch.chunk(partitions_pp[tpr][idx].data, 2, dim=0)
              )
  
          w_split = torch.cat([w[0] for w in wk_splits], dim=0)
          k_split = torch.cat([w[1] for w in wk_splits], dim=0)
          concated = torch.cat([w_split, k_split], dim=0)
  
      # Trim padding
      if concated.shape != param.shape:
          logging.info(
              f"Warning: Shape mismatch for parameter {name} required shape: {param.shape}, merged shape: {concated.shape}. Narrowing to match required size."
          )
          if concated.shape[1:] == param.shape[1:]:
              concated = torch.narrow(concated, 0, 0, param.shape[0])
          elif concated.shape[:-1] == param.shape[:-1]:
              concated = torch.narrow(concated, -1, 0, param.shape[-1])
          else:
              raise RuntimeError(
                  f"Can not handle parameter {name}, required shape: {param.shape}, merged shape: {concated.shape}."
              )
      return concated
  
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 5 for calling a global function with example usage: compute_tp_merge(idx, name, param, partitions_pp, model_cfg) and returns: concated