code: def grad_repeat(ans, x, repeats, axis=None):
      shape = anp.shape(x)
  
      def vjp(g):
          if (
              axis is None
          ):  # If axis is none, np.repeat() repeats the flattened array.
              expanded = anp.reshape(g, (anp.prod(shape),) + (repeats,))
              return anp.reshape(
                  anp.sum(expanded, axis=1, keepdims=False), shape
              )
          else:
              if (
                  shape[axis] == 1
              ):  # For this common case, the logic is simple.
                  return anp.sum(g, axis=axis, keepdims=True)
              else:
                  expanded = anp.reshape(
                      g,
                      shape[0 : axis + 1]
                      + (repeats,)
                      + shape[axis + 1 :],
                  )
                  return anp.sum(
                      expanded, axis=axis + 1, keepdims=False
                  )
  
      return vjp
  
imports: from __future__ import absolute_import
  from six import string_types
  from functools import partial
  import numpy
  from ..util import func
  from .None import numpy_wrapper
  from .numpy_boxes import ArrayBox
  from autograd.extend import primitive, vspace, defvjp, defvjp_argnum, SparseObject, VJPNode, register_notrace
label_desc: Use image node_id 4 for calling a global function with example usage: grad_repeat(ans, x, repeats, axis) and returns: vjp, anp, anp, anp