code: def _transpose(x, heads=1, mp_replace=None):
      heads = heads // mp_replace.mp_size  # type: ignore
      outer_dim = -1
      attention_head_size = x.shape[outer_dim] // heads
      new_x_shape = x.size()[:outer_dim] + (heads, attention_head_size)
      x_1 = x.view(*new_x_shape)
      (q, k, v) = torch.split(x_1, (x_1.shape[-1] // 3), dim=-1)
      if len(q.shape) > 2:
          new_shape = (q.shape[0],) + (-1,)
          return torch.cat(
              (
                  q.reshape(new_shape),
                  k.reshape(new_shape),
                  v.reshape(new_shape),
              ),
              dim=outer_dim,
          ).reshape(x.shape)
      else:
          return torch.cat(
              (q.reshape(-1), k.reshape(-1), v.reshape(-1)), dim=-1
          ).reshape(x.shape)
  
imports: from abc import ABC, abstractmethod
  from deepspeed.utils.types import ActivationFuncType, NormType
  import torch
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling a global function with example usage: _transpose(x, heads, mp_replace) and returns: torch, torch