code: def _stdtrit_bwd(aux, g):
      """Reverse mode implementation for stdtrit."""
      df, p = aux
      partial_df, partial_p = _stdtrit_partials(df, p)
      return generic.fix_gradient_for_broadcasting(
          [df, p], [partial_df * g, partial_p * g]
      )
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  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 generic
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import numeric
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 18 for calling a global function with example usage: _stdtrit_bwd(aux, g) and returns: generic