old_contents: from scipy import stats
  
  
  def uniform(low=0, high=1):
      """ Create a frozen uniform distribution
  
      Parameters
      ----------
      low, high : floats, optional
          Lower and upper limits of the distribution.
  
      Returns
      -------
      scipy.stats.uniform
  
      """
  
      return stats.uniform(loc=low, scale=high-low)
  
  
  def normal(mu=0, sigma=1):
      """ Create a frozen normal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the distribution.
      sigma : float
          The standard deviation (scale) of the distribution.
  
      Returns
      -------
      scipy.stats.normal
  
      """
  
      return stats.normal(loc=mu, scale=sigma)
  
  
  def lognormal(mu=0, sigma=1, offset=0):
      """ Create a frozen lognormal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the underlying normal distribution.
      sigma : float
          The standard deviation (scale) of the underlying normal
          distribution.
      offset : float, optional (default = 0)
          Lateral translation parameter availabe in scipy's implementation
          of the distribution. Highly recommended to leave this at zero.
  
      Returns
      -------
      scipy.stats.lognormal
  
      """
  
      return stats.lognorm(sigma, scale=np.exp(mu), loc=offset)
  
  
  def beta(alpha, beta):
      """ Create a frozen beta distribution
  
      Parameters
      ----------
      alpha, beta : floats
  
      Returns
      -------
      scipy.stats.beta
  
      """
  
      return stats.beta(alpha, beta)
  
  
  def chi_squared(k):
      """ Create a frozen chi_squared distribution
  
      Parameters
      ----------
      k : float
  
      Returns
      -------
      scipy.stats.chi_squared
  
      """
  
      return stats.chi2(k)
  
  
  def pareto(alpha):
      """ Create a frozen pareto distribution
  
      Parameters
      ----------
      alpha : float
  
      Returns
      -------
      scipy.stats.pareto
  
      """
  
      return stats.pareto(alpha)
  
  
  def gamma(k, theta):
      """ Create a frozen gamma distribution
  
      Parameters
      ----------
      k, theta : floats
  
      Returns
      -------
      scipy.stats.gamma
  
      """
  
      return stats.gamma(k, scape=theta)
  
new_contents: import numpy as np
  from scipy import stats
  
  
  def uniform(low=0, high=1):
      """ Create a frozen uniform distribution
  
      Parameters
      ----------
      low, high : floats, optional
          Lower and upper limits of the distribution.
  
      Returns
      -------
      scipy.stats.uniform
  
      """
  
      return stats.uniform(loc=low, scale=high-low)
  
  
  def normal(mu=0, sigma=1):
      """ Create a frozen normal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the distribution.
      sigma : float
          The standard deviation (scale) of the distribution.
  
      Returns
      -------
      scipy.stats.normal
  
      """
  
      return stats.norm(loc=mu, scale=sigma)
  
  
  def lognormal(mu=0, sigma=1, offset=0):
      """ Create a frozen lognormal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the underlying normal distribution.
      sigma : float
          The standard deviation (scale) of the underlying normal
          distribution.
      offset : float, optional (default = 0)
          Lateral translation parameter availabe in scipy's implementation
          of the distribution. Highly recommended to leave this at zero.
  
      Returns
      -------
      scipy.stats.lognormal
  
      """
  
      return stats.lognorm(sigma, scale=np.exp(mu), loc=offset)
  
  
  def beta(alpha, beta):
      """ Create a frozen beta distribution
  
      Parameters
      ----------
      alpha, beta : floats
  
      Returns
      -------
      scipy.stats.beta
  
      """
  
      return stats.beta(alpha, beta)
  
  
  def chi_squared(k):
      """ Create a frozen chi_squared distribution
  
      Parameters
      ----------
      k : float
  
      Returns
      -------
      scipy.stats.chi_squared
  
      """
  
      return stats.chi2(k)
  
  
  def pareto(alpha):
      """ Create a frozen pareto distribution
  
      Parameters
      ----------
      alpha : float
  
      Returns
      -------
      scipy.stats.pareto
  
      """
  
      return stats.pareto(alpha)
  
  
  def gamma(k, theta):
      """ Create a frozen gamma distribution
  
      Parameters
      ----------
      k, theta : floats
  
      Returns
      -------
      scipy.stats.gamma
  
      """
  
      return stats.gamma(k, scape=theta)
  
subject: fix typo in call to stats.norm and missing import