text: from typing import Tuple, Dict
  import numpy as np
  import tensorflow as tf
  from tensorflow import Tensor
  
  from decompose.distributions.algorithms import Algorithms
  
  
  class LaplaceAlgorithms(Algorithms):
  
      @classmethod
      def sample(cls, parameters: Dict[str, Tensor], nSamples: Tensor) -> Tensor:
          mu, beta = parameters["mu"], parameters["beta"]
          norm = tf.distributions.Laplace(loc=mu, scale=beta)
          r = norm.sample(sample_shape=(nSamples,))
          print("r in sample", r)
          return(r)
  
      @classmethod
      def mode(cls, parameters: Dict[str, Tensor]) -> Tensor:
          mu = parameters["mu"]
          return(mu)
  
      @classmethod
      def pdf(cls, parameters: Dict[str, Tensor], data: Tensor) -> Tensor:
          mu, beta = parameters["mu"], parameters["beta"]
          norm = tf.distributions.Laplace(loc=mu, scale=beta)
          pdf = norm.prob(value=data)
          return(pdf)
  
      @classmethod
      def fit(cls, parameters: Dict[str, Tensor],
              data: tf.Tensor) -> Dict[str, Tensor]:
          M = data.get_shape().as_list()[0]
          mu = tf.contrib.nn.nth_element(tf.transpose(data), M//2)
          beta = tf.reduce_mean(tf.abs(data-mu), axis=0)
          updatedParameters = {"mu": mu, "beta": beta}
          return(updatedParameters)
  
      @classmethod
      def llh(cls, parameters: Dict[str, Tensor], data: tf.Tensor) -> Tensor:
          mu, beta = parameters["mu"], parameters["beta"]
          norm = tf.distributions.Laplace(loc=mu, scale=beta)
          llh = norm.log_prob(value=data)
          return(llh)
  
      @classmethod
      def fitLatents(cls, parameters: Dict[str, Tensor],
                     data: Tensor) -> Dict[str, Tensor]:
          return({})
  