class_docstr: Chi distribution.
  
  The Chi distribution is defined over nonnegative real numbers and uses a
  degrees of freedom ('df') parameter.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; df, x >= 0) = x**(df - 1) exp(-0.5 x**2) / Z
  Z = 2**(0.5 df - 1) Gamma(0.5 df)
  ```
  
  where:
  
  * `df` denotes the degrees of freedom,
  * `Z` is the normalization constant, and,
  * `Gamma` is the [gamma function](
    https://en.wikipedia.org/wiki/Gamma_function).
  
  The Chi distribution is a transformation of the Chi2 distribution; it is the
  distribution of the positive square root of a variable obeying a Chi2
  distribution.
code: def _entropy(self):
      df = tf.convert_to_tensor(self.df)
      return tf.math.lgamma(0.5 * df) + 0.5 * (
          df - np.log(2.0) - (df - 1.0) * tf.math.digamma(0.5 * df)
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import square
  from tensorflow_probability.python.distributions import chi2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 6 for calling the Chi obj's underlying member method code with example usage: obj._entropy() and returns: unknown