code: def combine_heads(X):
      """
      Combine heads (the inverse of split heads):
      1) Transpose X from (batch size, nheads, sequence length, d_head) to
          (batch size, sequence length, nheads, d_head)
      2) Combine (reshape) last 2 dimensions (nheads, d_head) into 1 (d_model)
  
      Inputs:
        X : [batch size * nheads, sequence length, d_head]
        nheads : integer
        d_head : integer
  
      Outputs:
        [batch_size, seq_len, d_model]
  
      """
      X = X.transpose(1, 2)
      nheads, d_head = X.shape[-2:]
      return X.contiguous().view(list(X.shape[:-2]) + [nheads * d_head])
  
file_path: translate/pytorch_translate/research/attention/multihead_attention.py
imports: import math
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 5 for calling a global function with example usage: combine_heads(X) and returns: X