code: def cdf(value, df, loc, scale, skewness):
      """Compute the distribution 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 cdf.
        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)
      ]
  
      one = numpy_dtype(1.0)
      two = numpy_dtype(2.0)
  
      t = standardize(value, loc=loc, scale=scale, skewness=skewness)
      t_cdf = student_t.stdtr(df, t=t)
  
      squared_skewness = tf.math.square(skewness)
      return tf.math.reciprocal(one + squared_skewness) * tf.where(
          t < 0.0,
          two * t_cdf,
          one - squared_skewness + two * squared_skewness * t_cdf,
      )
  
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 3 for calling a global function with example usage: cdf(value, df, loc, scale, skewness) and returns: unknown