parameters: (self,
                   bijector,
                   distribution,
                   seed=None,
                   name="transformed_distribution")
docstring: Constructor of transformed_distribution class.
docstring_summary: Constructor of transformed_distribution class.
docstring_tokens: Constructor of transformed_distribution class .
function: def __init__(self,
                   bijector,
                   distribution,
                   seed=None,
                   name="transformed_distribution"):
          """
          Constructor of transformed_distribution class.
          """
          param = dict(locals())
          validator.check_value_type('bijector', bijector,
                                     [nn.probability.bijector.Bijector], type(self).__name__)
          validator.check_value_type('distribution', distribution,
                                     [Distribution], type(self).__name__)
          validator.check_type_name(
              "dtype", distribution.dtype, mstype.float_type, type(self).__name__)
          super(TransformedDistribution, self).__init__(
              seed, distribution.dtype, name, param)
  
          self._bijector = bijector
          self._distribution = distribution
  
          # set attributes
          self._is_linear_transformation = self.bijector.is_constant_jacobian
          self._dtype = self.distribution.dtype
          self._is_scalar_batch = self.distribution.is_scalar_batch and self.bijector.is_scalar_batch
          self._batch_shape = self.distribution.batch_shape
  
          self.default_parameters = self.distribution.default_parameters
          self.parameter_names = self.distribution.parameter_names
          # by default, set the parameter_type to be the distribution's parameter_type
          self.parameter_type = self.distribution.parameter_type
  
          self.exp = exp_generic
          self.log = log_generic
          self.isnan = P.IsNan()
          self.cast_base = P.Cast()
          self.equal_base = P.Equal()
          self.select_base = P.Select()
          self.fill_base = P.Fill()
  
          # broadcast bijector batch_shape and distribution batch_shape
          self._broadcast_shape = self._broadcast_bijector_dist()
function_tokens: def __init__ ( self , bijector , distribution , seed = None , name = "transformed_distribution" ) : param = dict ( locals ( ) ) validator . check_value_type ( 'bijector' , bijector , [ nn . probability . bijector . Bijector ] , type ( self ) . __name__ ) validator . check_value_type ( 'distribution' , distribution , [ Distribution ] , type ( self ) . __name__ ) validator . check_type_name ( "dtype" , distribution . dtype , mstype . float_type , type ( self ) . __name__ ) super ( TransformedDistribution , self ) . __init__ ( seed , distribution . dtype , name , param ) self . _bijector = bijector self . _distribution = distribution # set attributes self . _is_linear_transformation = self . bijector . is_constant_jacobian self . _dtype = self . distribution . dtype self . _is_scalar_batch = self . distribution . is_scalar_batch and self . bijector . is_scalar_batch self . _batch_shape = self . distribution . batch_shape self . default_parameters = self . distribution . default_parameters self . parameter_names = self . distribution . parameter_names # by default, set the parameter_type to be the distribution's parameter_type self . parameter_type = self . distribution . parameter_type self . exp = exp_generic self . log = log_generic self . isnan = P . IsNan ( ) self . cast_base = P . Cast ( ) self . equal_base = P . Equal ( ) self . select_base = P . Select ( ) self . fill_base = P . Fill ( ) # broadcast bijector batch_shape and distribution batch_shape self . _broadcast_shape = self . _broadcast_bijector_dist ( )