class_docstr: Compute `Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c`.
  
  The [power transform](https://en.wikipedia.org/wiki/Power_transform) maps
  inputs from `[0, inf]` to `[-1/c, inf]`; this is equivalent to the `inverse`
  of this bijector.
  
  This bijector is equivalent to the `Exp` bijector when `c=0`.
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code: def _inverse(self, y):
      with tf.control_dependencies(self._maybe_assert_valid_y(y)):
          if self.power == 0.0:
              return tf.math.log(y)
          # If large y accuracy is an issue, consider using:
          # (y**self.power - 1.) / self.power when y >> 1.
          power = tf.cast(self.power, y.dtype)
          return tf.math.expm1(tf.math.log(y) * power) / power
  
file_path: probability/tensorflow_probability/python/bijectors/power_transform.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import prefer_static
label_desc: Use image node_id 6 for calling the PowerTransform obj's underlying member method code with example usage: obj._inverse(y) and returns: unknown, tf