class_docstr: The Weibull distribution with 'concentration' and `scale` parameters.
  
  #### Mathematical details
  
  The probability density function (pdf) of this distribution is,
  
  ```none
  pdf(x; lambda, k) =
    k / lambda * (x / lambda) ** (k - 1) * exp(-(x / lambda) ** k)
  ```
  
  where `concentration = k` and `scale = lambda`.
  
  The cumulative density function of this distribution is,
  
  ```none
  cdf(x; lambda, k) = 1 - exp(-(x / lambda) ** k)
  ```
  
  The Weibull distribution includes the Exponential and Rayleigh distributions
  as special cases:
  
  ```none
  Exponential(rate) = Weibull(concentration=1., 1. / rate)
  ```
  
  ```none
  Rayleigh(scale) = Weibull(concentration=2., sqrt(2.) * scale)
  ```
  
  #### Examples
  
  Example of initialization of one distribution.
  
  ```python
  tfd = tfp.distributions
  
  # Define a single scalar Weibull distribution.
  dist = tfd.Weibull(concentration=1., scale=3.)
  
  # Evaluate the cdf at 1, returning a scalar.
  dist.cdf(1.)
  ```
  
  Example of initialization of a 3-batch of distributions with varying scales
  and concentrations.
  
  ```python
  tfd = tfp.distributions
  
  # Define a 3-batch of Weibull distributions.
  scale = [1., 3., 45.]
  concentration = [2.5, 22., 7.]
  dist = tfd.Weibull(concentration=concentration, scale=scale)
  
  # Evaluate the cdfs at 1.
  dist.cdf(1.)    # shape: [3]
  ```
class_docstr_tok: ["The","Weibull","distribution","with","'concentration","'","and","`","scale","`","parameters",".","#","#","#","#","Mathematical","details","The","probability","density","function","(","pdf",")","of","this","distribution","is",",","``","`","none","pdf","(","x",";","lambda",",","k",")","=","k","\/","lambda","*","(","x","\/","lambda",")","*","*","(","k","-","1",")","*","exp","(","-","(","x","\/","lambda",")","*","*","k",")","``","`","where","`","concentration","=","k","`","and","`","scale","=","lambda","`",".","The","cumulative","density","function","of","this","distribution","is",",","``","`","none","cdf","(","x",";","lambda",",","k",")","=","1","-","exp","(","-","(","x","\/","lambda",")","*","*","k",")","``","`","The","Weibull","distribution","includes","the","Exponential","and","Rayleigh","distributions","as","special","cases",":","``","`","none","Exponential","(","rate",")","=","Weibull","(","concentration=1.",",","1",".","\/","rate",")","``","`","``","`","none","Rayleigh","(","scale",")","=","Weibull","(","concentration=2.",",","sqrt","(","2",".",")","*","scale",")","``","`","#","#","#","#","Examples","Example","of","initialization","of","one","distribution",".","``","`","python","tfd","=","tfp.distributions","#","Define","a","single","scalar","Weibull","distribution",".","dist","=","tfd.Weibull","(","concentration=1.",",","scale=3",".",")","#","Evaluate","the","cdf","at","1",",","returning","a","scalar",".","dist.cdf","(","1",".",")","``","`","Example","of","initialization","of","a","3-batch","of","distributions","with","varying","scales","and","concentrations",".","``","`","python","tfd","=","tfp.distributions","#","Define","a","3-batch","of","Weibull","distributions",".","scale","=","[","1.",",","3.",",","45",".","]","concentration","=","[","2.5",",","22.",",","7",".","]","dist","=","tfd.Weibull","(","concentration=concentration",",","scale=scale",")","#","Evaluate","the","cdfs","at","1",".","dist.cdf","(","1",".",")","#","shape",":","[","3","]","``","`"]
code: def _mean(self):
      one = tf.constant(1.0, dtype=self.dtype)
      return self.scale * tf.exp(
          tf.math.lgamma(one + tf.math.reciprocal(self.concentration))
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import weibull_cdf
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.distributions.gamma import Gamma
  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 tensor_util
label_desc: Use image node_id 7 for calling the Weibull obj's underlying member method code with example usage: obj._mean() and returns: unknown