class_docstr: RelaxedOneHotCategorical distribution with temperature and logits.
  
  The RelaxedOneHotCategorical is a distribution over random probability
  vectors, vectors of positive real values that sum to one, which continuously
  approximates a OneHotCategorical. The degree of approximation is controlled by
  a temperature: as the temperature goes to 0 the RelaxedOneHotCategorical
  becomes discrete with a distribution described by the `logits` or `probs`
  parameters, as the temperature goes to infinity the RelaxedOneHotCategorical
  becomes the constant distribution that is identically the constant vector of
  (1/event_size, ..., 1/event_size).
  
  The RelaxedOneHotCategorical distribution was concurrently introduced as the
  Gumbel-Softmax (Jang et al., 2016) and Concrete (Maddison et al., 2016)
  distributions for use as a reparameterized continuous approximation to the
  `Categorical` one-hot distribution. If you use this distribution, please cite
  both papers.
  
  #### Examples
  
  Creates a continuous distribution, which approximates a 3-class one-hot
  categorical distribution. The 2nd class is the most likely to be the
  largest component in samples drawn from this distribution.
  
  ```python
  temperature = 0.5
  p = [0.1, 0.5, 0.4]
  dist = RelaxedOneHotCategorical(temperature, probs=p)
  ```
  
  Creates a continuous distribution, which approximates a 3-class one-hot
  categorical distribution. The 2nd class is the most likely to be the
  largest component in samples drawn from this distribution.
  
  ```python
  temperature = 0.5
  logits = [-2, 2, 0]
  dist = RelaxedOneHotCategorical(temperature, logits=logits)
  ```
  
  Creates a continuous distribution, which approximates a 3-class one-hot
  categorical distribution. Because the temperature is very low, samples from
  this distribution are almost discrete, with one component almost 1 and the
  others nearly 0. The 2nd class is the most likely to be the largest component
  in samples drawn from this distribution.
  
  ```python
  temperature = 1e-5
  logits = [-2, 2, 0]
  dist = RelaxedOneHotCategorical(temperature, logits=logits)
  ```
  
  Creates a continuous distribution, which approximates a 3-class one-hot
  categorical distribution. Because the temperature is very high, samples from
  this distribution are usually close to the (1/3, 1/3, 1/3) vector. The 2nd
  class is still the most likely to be the largest component
  in samples drawn from this distribution.
  
  ```python
  temperature = 10
  logits = [-2, 2, 0]
  dist = RelaxedOneHotCategorical(temperature, logits=logits)
  ```
  
  Eric Jang, Shixiang Gu, and Ben Poole. Categorical Reparameterization with
  Gumbel-Softmax. 2016.
  
  Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The Concrete Distribution:
  A Continuous Relaxation of Discrete Random Variables. 2016.
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docstr: Probs vec computed from non-`None` input arg (`probs` or `logits`).
docstr_tok: ["Probs","vec","computed","from","non-","`","None","`","input","arg","(","`","probs","`","or","`","logits","`",")","."]
code: def probs_parameter(self, name=None):
      """Probs vec computed from non-`None` input arg (`probs` or `logits`)."""
      return self.distribution.probs_parameter(name)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softmax_centered
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  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 prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 8 for calling the RelaxedOneHotCategorical obj's underlying member method code with example usage: obj.probs_parameter(name) and returns: self