code: def standardize(value, loc, scale, skewness):
      """Apply mean-variance-skewness standardization to input `value`.
  
      Note that scale and skewness can be negative.
  
      Args:
        value: Floating-point tensor; the value(s) to be standardized.
        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.
      """
      value = tf.convert_to_tensor(value)
      loc = tf.convert_to_tensor(loc)
      scale = tf.convert_to_tensor(scale)
      skewness = tf.convert_to_tensor(skewness)
  
      return (
          (value - loc)
          / tf.math.abs(scale)
          * tf.math.abs(
              tf.where(
                  value < loc, skewness, tf.math.reciprocal(skewness)
              )
          )
      )
  
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.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  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 special
  from tensorflow_probability.python.math.numeric import log1psquare
label_desc: Use image node_id 2 for calling a global function with example usage: standardize(value, loc, scale, skewness) and returns: unknown