code: def draw(self):
          '''
          Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.
          '''
          observed_arr = self.extract_conditions()
          conv_arr = self.inference(observed_arr)
  
          if self.__conditon_noise_sampler is not None:
              self.__conditon_noise_sampler.output_shape = conv_arr.shape
              noise_arr = self.__conditon_noise_sampler.generate()
              conv_arr += noise_arr
  
          deconv_arr = self.__deconvolution_model.inference(conv_arr)
          return np.concatenate((deconv_arr, observed_arr), axis=1f draw(self):
          '''
          Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.
          '''
          observed_arr = self.extract_conditions()
          conv_arr = self.inference(observed_arr)
  
          if self.__conditon_noise_sampler is not None:
              self.__conditon_noise_sampler.output_shape = conv_arr.shape
              noise_arr = self.__conditon_noise_sampler.generate()
              conv_arr += noise_arr
  
          deconv_arr = self.__deconvolution_model.inference(conv_arr)
          return np.concatenate((deconv_arr, observed_arr), axis=1)
docs: Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.