code: def t_power(logu, t, self_normalized=False, name=None):
      """The T-Power Csiszar-function in log-space.
  
      A Csiszar-function is a member of,
  
      ```none
      F = { f:R_+ to R : f convex }.
      ```
  
      When `self_normalized = True` the T-Power Csiszar-function is:
  
      ```none
      f(u) = s [ u**t - 1 - t(u - 1) ]
      s = { -1   0 < t < 1
          { +1   otherwise
      ```
  
      When `self_normalized = False` the `- t(u - 1)` term is omitted.
  
      This is similar to the `amari_alpha` Csiszar-function, with the associated
      divergence being the same up to factors depending only on `t`.
  
      Args:
        logu: `float`-like `Tensor` representing `log(u)` from above.
        t:  `Tensor` of same `dtype` as `logu` and broadcastable shape.
        self_normalized: Python `bool` indicating whether `f'(u=1)=0`.
        name: Python `str` name prefixed to Ops created by this function.
  
      Returns:
        t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
          at `u = exp(logu)`.
      """
      with tf.name_scope(name or "t_power"):
          logu = tf.convert_to_tensor(logu, name="logu")
          t = tf.convert_to_tensor(
              t, dtype=dtype_util.base_dtype(logu.dtype), name="t"
          )
          fu = tf.math.expm1(t * logu)
          if self_normalized:
              fu = fu - t * tf.math.expm1(logu)
          return tf.where((0 < t) & (t < 1), -fu, fu)
  
imports: import enum
  import functools
  import warnings
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import monte_carlo
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal.reparameterization import FULLY_REPARAMETERIZED
  from tensorflow_probability.python.stats.leave_one_out import log_soomean_exp
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 11 for calling a global function with example usage: t_power(logu, t, _normalized, name) and returns: tf