class_docstr: Modified PERT distribution for modeling expert predictions.
  
  The PERT distribution is a loc-scale family of Beta distributions
  fit onto a real interval between `low` and `high` values set by the user,
  along with a `peak` to indicate the expert's most frequent prediction.
  [1](https://en.wikipedia.org/wiki/PERT_distribution), and `temperature` to
  control how sharp the peak is.
  
  The distribution is similar to a [Triangular distribution]
  (https://en.wikipedia.org/wiki/Triangular_distribution)
  (i.e. `tfd.Triangular`) but with a smooth peak.
  
  #### Mathematical Details
  
  In terms of a Beta distribution, PERT can be expressed as
  
  ```none
  PERT ~ loc + scale * Beta(concentration1, concentration0)
  ```
  where
  
  ```none
  loc = low
  scale = high - low
                                      peak - low
  concentration1 = 1 + temperature * ------------
                                      high - low
  
                                      high - peak
  concentration0 = 1 + temperature * ------------
                                      high - low
  temperature > 0
  ```
  The support is `[low, high]`.  The `peak` must fit in that interval:
  `low < peak < high`.  The `temperature` is a positive parameter that
  controls the shape of the distribution. Higher values yield a sharper peak.
  
  The standard PERT distribution is obtained when `temperature = 4`.
  
  #### Examples
  ```python
  import tensorflow_probability as tfp
  tfd = tfp.distributions
  
  # Single PERT distribution
  dist = tfd.PERT(low=1., peak=7., high=11., temperature=4.)
  dist.sample(10)
  dist.prob(7.)
  dist.prob(0.) # Returns nan when the input is outside the support.
  
  # Multiple PERT distributions with varying temperature (broadcasted)
  dist = tfd.PERT(low=1., peak=7., high=11., temperature=[1., 2., 3., 4.])
  dist.sample(10)
  dist.prob(7.)
  dist.prob([[7.],[5.]])
  
  # Multiple PERT distributions with varying peak
  dist = tfd.PERT(low=1., peak=[2., 5., 8.], high=11., temperature=4.)
  dist.sample(10)
  dist.sample([10, 5])
  ```
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code: def temperature(self):
      return self._temperature
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import beta
  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 tensor_util
label_desc: Use image node_id 7 for calling the PERT obj's underlying member method code with example usage: obj.temperature() and returns: self