code: def _fast_matrix_shift(x, funnel_factor=1, is_upsampling=False):
      """Fast matrix shift."""
  
      if funnel_factor == 1 and not is_upsampling:
          shift = 1
          batch_size, n_head = x.shape[0], x.shape[1]
          queries_len, keys_len = x.shape[2], x.shape[3]
          zero_pad = jnp.zeros((batch_size, n_head, queries_len, shift))
          x = jnp.concatenate([zero_pad, x], axis=3)
          x = x.reshape(
              batch_size, n_head, keys_len + shift, queries_len
          )
          x = x[:, :, shift:, :]
          return x
  
      if is_upsampling:
          k = funnel_factor
          shift = 1
      else:
          k = 1
          shift = funnel_factor
  
      bsz, n_head = x.shape[0], x.shape[1]
      qlen, klen = x.shape[2], (x.shape[3] + 1) // 2
  
      zero_pad = jnp.zeros((bsz, n_head, qlen, shift))
      x = jnp.concatenate([zero_pad, x], axis=3)
      x = x.reshape(bsz, n_head, 2 * klen - 1 + shift, qlen)
      x = x[:, :, shift:, :]
      x = x.reshape(bsz, n_head, qlen, klen * 2 - 1)
      x = x[:, :, :, shift - 1 : shift - 1 + klen : k]
      return x
  
imports: from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.attention import MergeHeads
  from trax.layers.attention import SplitIntoHeads
label_desc: Use image node_id 8 for calling a global function with example usage: _fast_matrix_shift(x, funnel_factor, is_upsampling) and returns: x, x