code: def _nc2_quantile_bwd(aux, g):
      output, p, df, noncentrality = aux
  
      # Compute the gradient for `p`
      grad_p = (
          tf.math.exp(-_nc2_log_prob(output, df, noncentrality)) * g
      )
      grad_p = generic.fix_gradient_for_broadcasting([p], [grad_p])
  
      return grad_p, None, None, None
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import chi2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import bessel
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import root_search
  import tensorflow_probability.python.random
label_desc: Use image node_id 10 for calling a global function with example usage: _nc2_quantile_bwd(aux, g) and returns: grad_p,  None,  None,  None