code: def SplitIntoHeads(n_heads, merged_batch_and_head=True):
      """Returns a layer that reshapes an array for multi-head computation."""
  
      def f(x):
          batch_size, seq_len, d_feature = x.shape
          if d_feature % n_heads != 0:
              raise ValueError(
                  f"Feature embedding dimensionality ({d_feature}) is not a multiple"
                  f" of the requested number of attention heads ({n_heads})."
              )
  
          d_head = d_feature // n_heads
  
          # (b_size, seq_len, d_feature) --> (b_size*n_heads, seq_len, d_head)
          x = x.reshape((batch_size, seq_len, n_heads, d_head))
          x = x.transpose((0, 2, 1, 3))
          if merged_batch_and_head:
              x = x.reshape((batch_size * n_heads, seq_len, d_head))
          return x
  
      return Fn("SplitIntoHeads", f)
  
imports: import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
  from trax.layers.research import sparsity
label_desc: Use image node_id 4 for calling a global function with example usage: SplitIntoHeads(n_heads, merged_batch_and_head) and returns: Fn, x