code: def combine_heads(
      F, x: Tensor, dim_per_head: int, heads: int
  ) -> Tensor:
      r"""
  
      Parameters
      ----------
      x
          Tensor of shape (batch_size * heads, time_length, dim_per_head)
      dim_per_head
          Dimension per head
      heads
          Number of heads
  
      Returns
      -------
      Tensor of shape (batch_size, time_length, dim)
      """
  
      # (batch_size, heads, time_length, dim_per_head)
      x = F.reshape(data=x, shape=(-4, -1, heads, 0, dim_per_head))
      # (batch_size, time_length, heads, dim_per_head)
      x = F.transpose(x, axes=(0, 2, 1, 3))
      # (batch_size, time_length, dim)
      return F.reshape(x, shape=(-1, 0, dim_per_head * heads))
  
imports: from typing import Dict, Optional, Tuple
  import mxnet
  from mxnet.gluon import HybridBlock
  from gluonts.mx import Tensor
label_desc: Use image node_id 3 for calling a global function with example usage: combine_heads(F, x, dim_per_head, heads) and returns: F