original_docstring: r"""
  
          Draws random samples from the Gamma distribution in shape `size`
  
          Parameters
          ----------
  
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          random : scalar or numpy array
              Random values drawn from the distribution in shape `size`
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> Gamma.random(10, 3, 4)
          array([0.22856155, 1.69542468, 0.70894789, 0.75552168, 0.76634128,
                 0.58624638, 1.03288812, 0.85768925, 0.75071764, 0.91979151])
          >>> Gamma.random((5, 5), 3, 4)
          array([[0.55481976, 1.02867642, 1.25525161, 0.5141736 , 0.7227451 ],
                 [1.59192864, 1.22897457, 0.80820007, 0.39872068, 0.53656654],
                 [0.80703614, 0.75406597, 0.87307426, 1.88748737, 0.78115455],
                 [1.3233755 , 0.29908068, 1.88304902, 2.65690385, 0.51018073],
                 [0.36070265, 0.48834586, 0.45623895, 0.30104303, 0.49942908]])
          
docstring: r"""
  Draws random samples from the Gamma distribution in shape `size`
  
  Parameters
  
  alpha : numpy array or scalar
  The shape parameter for the Gamma distribution
  beta : numpy array or scalar
  The scale parameter for the Gamma distribution
  
  Returns
  
  random : scalar or numpy array
  Random values drawn from the distribution in shape `size`
  
  Examples
  
  
docstring_tokens: r " " " Draws random samples from the Gamma distribution in shape ` size ` Parameters alpha : numpy array or scalar The shape parameter for the Gamma distribution beta : numpy array or scalar The scale parameter for the Gamma distribution Returns random : scalar or numpy array Random values drawn from the distribution in shape ` size ` Examples
code: def random(self, size, alpha, beta):
          r"""
  
          Draws random samples from the Gamma distribution in shape `size`
  
          Parameters
          ----------
  
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          random : scalar or numpy array
              Random values drawn from the distribution in shape `size`
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> Gamma.random(10, 3, 4)
          array([0.22856155, 1.69542468, 0.70894789, 0.75552168, 0.76634128,
                 0.58624638, 1.03288812, 0.85768925, 0.75071764, 0.91979151])
          >>> Gamma.random((5, 5), 3, 4)
          array([[0.55481976, 1.02867642, 1.25525161, 0.5141736 , 0.7227451 ],
                 [1.59192864, 1.22897457, 0.80820007, 0.39872068, 0.53656654],
                 [0.80703614, 0.75406597, 0.87307426, 1.88748737, 0.78115455],
                 [1.3233755 , 0.29908068, 1.88304902, 2.65690385, 0.51018073],
                 [0.36070265, 0.48834586, 0.45623895, 0.30104303, 0.49942908]])
          """
          U = uniform.rvs(size=size)
          return self.qf(U, alpha, beta)
code_tokens: def random ( self , size , alpha , beta ) : U = uniform . rvs ( size = size ) return self . qf ( U , alpha , beta )
short_docstring: r"""
  Draws random samples from the Gamma distribution in shape `size`
short_docstring_tokens: r " " " Draws random samples from the Gamma distribution in shape ` size `
comment: r"""
  
          Draws random samples from the Gamma distribution in shape `size`
  
          Parameters
          ----------
  
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          random : scalar or numpy array
              Random values drawn from the distribution in shape `size`
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> Gamma.random(10, 3, 4)
          array([0.22856155, 1.69542468, 0.70894789, 0.75552168, 0.76634128,
                 0.58624638, 1.03288812, 0.85768925, 0.75071764, 0.91979151])
          >>> Gamma.random((5, 5), 3, 4)
          array([[0.55481976, 1.02867642, 1.25525161, 0.5141736 , 0.7227451 ],
                 [1.59192864, 1.22897457, 0.80820007, 0.39872068, 0.53656654],
                 [0.80703614, 0.75406597, 0.87307426, 1.88748737, 0.78115455],
                 [1.3233755 , 0.29908068, 1.88304902, 2.65690385, 0.51018073],
                 [0.36070265, 0.48834586, 0.45623895, 0.30104303, 0.49942908]])
          """