code: def quantile(value, df, loc, scale, skewness):
      """Compute the quantile function of Two-Piece Student's t-distribution.
  
      Note that `scale` and `skewness` can be negative.
  
      Args:
        value: Floating-point tensor; where to compute the quantile function.
        df: Floating-point tensor; the degree(s) of freedom of the
          distribution(s). Must contain only positive values.
        loc: Floating-point tensor; the location(s) of the distribution(s).
        scale: Floating-point tensor; the scale(s) of the distribution(s).
        skewness: Floating-point tensor; the skewness(es) of the distribution(s).
  
      Returns:
        A tensor with shape broadcast according to the arguments.
      """
      dtype = dtype_util.common_dtype(
          [value, df, loc, scale, skewness], tf.float32
      )
      numpy_dtype = dtype_util.as_numpy_dtype(dtype)
  
      value, df, loc, scale, skewness = [
          tf.convert_to_tensor(param, dtype=dtype)
          for param in (value, df, loc, scale, skewness)
      ]
  
      half = numpy_dtype(0.5)
      one = numpy_dtype(1.0)
  
      use_symmetry = value >= half
      value = tf.where(use_symmetry, one - value, value)
      loc = tf.where(use_symmetry, -loc, loc)
      skewness = tf.where(
          use_symmetry, tf.math.reciprocal(skewness), skewness
      )
  
      squared_skewness = tf.math.square(skewness)
      one_plus_squared_skewness = one + squared_skewness
      value_is_small = value <= tf.math.reciprocal(
          one_plus_squared_skewness
      )
  
      probs = half * value * one_plus_squared_skewness
      probs = tf.where(
          value_is_small,
          probs,
          (probs - half) / squared_skewness + half,
      )
      t_quantile = student_t.stdtrit(df, p=probs)
  
      abs_skewness = tf.math.abs(skewness)
      adj_scale = tf.math.abs(scale) * tf.where(
          value_is_small, tf.math.reciprocal(abs_skewness), abs_skewness
      )
  
      result = loc + adj_scale * t_quantile
  
      return tf.where(use_symmetry, -result, result)
  
imports: 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.distributions import student_t
  from tensorflow_probability.python.distributions import two_piece_normal
  from tensorflow_probability.python.internal import assert_util
  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 tensor_util
  from tensorflow_probability.python.math import special
  from tensorflow_probability.python.math.numeric import log1psquare
label_desc: Use image node_id 4 for calling a global function with example usage: quantile(value, df, loc, scale, skewness) and returns: tf