code: def _stdtrit_betaincinv(df, p, numpy_dtype, use_betaincinv):
      """Compute `stdtrit(df, p)` using special.betaincinv."""
      # This function inverts the procedure that computes stdtr(df, t) using the
      # regularized incomplete beta function. For details on this procedure, see
      # the function _stdtr_computation.
      # We assume here that condition (p <= 0.5) is always true.
      half = numpy_dtype(0.5)
      one = numpy_dtype(1.0)
  
      half_df = half * df
      two_p = numpy_dtype(2.0) * p
  
      use_symmetry_relation = p > (
          half * special.betainc(half_df, half, numpy_dtype(0.99))
      )
      a = tf.where(use_symmetry_relation, half, half_df)
      b = tf.where(use_symmetry_relation, half_df, half)
      y = tf.where(use_symmetry_relation, one - two_p, two_p)
  
      # Prepare betaincinv inputs to make its evaluation easier.
      a = tf.where(use_betaincinv, a, half)
      b = tf.where(use_betaincinv, b, half)
      y = tf.where(use_betaincinv, y, numpy_dtype(0.0))
  
      x = special.betaincinv(a, b, y)
  
      log_abs_t = half * (
          tf.math.log(df)
          + tf.where(use_symmetry_relation, one, -one)
          * (tf.math.log(x) - tf.math.log1p(-x))
      )
  
      return -tf.math.exp(log_abs_t)
  
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 13 for calling a global function with example usage: _stdtrit_betaincinv(df, p, numpy_dtype, use_betaincinv) and returns: unknown