code: def grad_np_std(ans, x, axis=None, ddof=0, keepdims=False):
      shape, _, dtype, iscomplex = anp.metadata(x)
  
      def vjp(g):
          if iscomplex:
              g = g + 0j
          g_repeated, num_reps = repeat_to_match_shape(
              g, shape, dtype, axis, keepdims
          )  # Avoid division by zero.
          if num_reps <= 1:
              return g_repeated * 0.0
          else:
              g_repeated, num_reps = repeat_to_match_shape(
                  g / ans, shape, dtype, axis, keepdims
              )
              x_minus_mean = anp.conj(
                  x - anp.mean(x, axis=axis, keepdims=True)
              )
              return g_repeated * x_minus_mean / (num_reps - ddof)
  
      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 14 for calling a global function with example usage: grad_np_std(ans, x, axis, ddof, keepdims) and returns: vjp, unknown, unknown