code: def smootherstep(x, name=None):
      """Computes a sigmoid-like interpolation function on the unit-interval.
  
      Equivalent to:
  
      ```python
      x = tf.clip_by_value(x, clip_value_min=0., clip_value_max=1.)
      y = x**3. * (6. * x**2. - 15. * x + 10.)
      ```
  
      For more details see [Wikipedia][1].
  
      Args:
        x: `float` `Tensor`.
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'smootherstep'`).
  
      Returns:
        smootherstep: `float` `Tensor` with the same shape and dtype as `x`,
          representing the value of the smootherstep function.
  
      #### References
  
      [1]: "Smoothstep." Wikipedia.
           https://en.wikipedia.org/wiki/Smoothstep#Variations
      """
      with tf.name_scope(name or "smootherstep"):
          x = tf.clip_by_value(
              x, clip_value_min=0.0, clip_value_max=1.0
          )
          # Note: Grappler will rewrite:
          #   x**2, x**3
          # as:
          #   x2 = tf.square(x)
          #   x3 = tf.square(x) * x
          # and common subexpression elimination (CSE) will produce:
          #   x2 = tf.square(x)
          #   x3 = x2 * x
          return x**3.0 * (6.0 * x**2.0 - 15.0 * x + 10.0)
  
imports: import collections
  import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import variadic_reduce
  from tensorflow_probability.python.math.scan_associative import scan_associative
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 13 for calling a global function with example usage: smootherstep(x, name) and returns: unknown