code: def _transpose_first_dim(t, num_splits, num_splits_first, model):
      input_shape = t.size()
      # We use a self_attention module but the values extracted aren't
      # specific to self attention so should work for cross attention as well
      while hasattr(model, "module"):
          model = model.module
      attention_module = model.language_model.encoder.layers[
          0
      ].self_attention
      hidden_size_per_attention_head = (
          attention_module.hidden_size_per_attention_head
      )
      num_attention_heads_per_partition = (
          attention_module.num_attention_heads_per_partition
      )
      if num_splits_first:
          """[num_splits * np * hn, h]
          -->(view) [num_splits, np, hn, h]
          -->(tranpose) [np, num_splits, hn, h]
          -->(view) [np * num_splits * hn, h]"""
  
          intermediate_shape = (
              num_splits,
              num_attention_heads_per_partition,
              hidden_size_per_attention_head,
          ) + input_shape[1:]
  
          t = t.view(*intermediate_shape)
          t = t.transpose(0, 1).contiguous()
      else:
          """[np * hn * num_splits, h]
          -->(view) [np, hn, num_splits, h]
          -->(tranpose) [np, num_splits, hn, h]
          -->(view) [np * num_splits * hn, h]"""
  
          intermediate_shape = (
              num_attention_heads_per_partition,
              hidden_size_per_attention_head,
              num_splits,
          ) + input_shape[1:]
  
          t = t.view(*intermediate_shape)
          t = t.transpose(1, 2).contiguous()
      t = t.view(*input_shape)
  
      return t
  
imports: import os
  import random
  import sys
  import numpy
  import torch
  from megatron import update_num_microbatches
  from megatron.core import mpu, tensor_parallel
  from .global_vars import get_args
  from .utils import unwrap_model, print_rank_0
label_desc: Use image node_id 12 for calling a global function with example usage: _transpose_first_dim(t, num_splits, num_splits_first, model) and returns: t