class_docstr: Chi2 distribution.
  
  The Chi2 distribution is defined over positive real numbers using a degrees of
  freedom ('df') parameter.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; df, x > 0) = x**(0.5 df - 1) exp(-0.5 x) / Z
  Z = 2**(0.5 df) 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 Chi2 distribution is a special case of the Gamma distribution -- i.e.,
  `Chi2(df)` represents the same distribution as
  `Gamma(concentration=0.5 * df, rate=0.5)`.
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code: def _parameter_control_dependencies(self, is_init):
      if not self.validate_args:
          return []
      assertions = []
      if is_init != tensor_util.is_ref(self.df):
          assertions.append(
              assert_util.assert_positive(
                  self.df, message="Argument `df` must be positive."
              )
          )
      return assertions
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 16 for calling the Chi2 obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list