class_docstr: Noncentral Chi2 distribution.
  
  The Noncentral Chi2 distribution is defined over positive real numbers using a
  degrees of freedom ('df') and a noncentrality parameter.
  
  The Noncentral Chi2 arises as the distribution of the squared Euclidean norm
  of a 'df'-dimensional multivariate Gaussian distribution with mean vector
  'mu' and identity covariance. The noncentrality parameter is given by the
  squared Euclidean norm of the mean vector 'mu'.
  
  #### Mathematical Details
  
  The probability density function (pdf) is
  
  ```none
  pdf(x; df, nc, x > 0)
    = 0.5 * (x/nc)**(df/4 - 1/2) * exp(-(x + nc)/2) * I_{k/2 - 1}(sqrt(nc * x))
  ```
  
  where:
  
  * `df` denotes the degrees of freedom,
  * `nc` denotes the noncentrality
  * `I_{a}` is the modified Bessel function of the first kind of order `a`.
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code: def _parameter_properties(cls, dtype, num_classes=None):
      return dict(
          df=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus_bijector.Softplus(
                      low=dtype_util.eps(dtype)
                  )
              )
          ),
          noncentrality=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus_bijector.Softplus(
                      low=dtype_util.eps(dtype)
                  )
              )
          ),
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import chi2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  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 prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import bessel
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import root_search
  import tensorflow_probability.python.random
label_desc: Use image node_id 2 for calling the NoncentralChi2 obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype, num_classes) and returns: dict