class_docstr: Inverse gamma distribution with an upper bound on sampled values.
code: def __init__(self, concentration, scale, upper_bound, **kwargs):
      self._upper_bound = upper_bound
      super().__init__(
          concentration=concentration, scale=scale, **kwargs
      )
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.distributions.mvn_precision_factor_linop import MultivariateNormalPrecisionFactorLinearOperator
  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 samplers
  from tensorflow_probability.python.internal import vectorization_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 to create a new InverseGammaWithSampleUpperBound object from inherited base classes: inverse_gamma with example: obj = InverseGammaWithSampleUpperBound(concentration, scale, upper_bound)