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 _sample_control_dependencies(self, x):
      assertions = []
      if not self.validate_args:
          return assertions
      assertions.append(
          assert_util.assert_non_negative(
              x, message="Sample must be non-negative."
          )
      )
      return assertions
  
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 9 for calling the Chi obj's underlying member method code with example usage: obj._sample_control_dependencies(x) and returns: assertions, assertions