concepts.pdsketch.generator.Generator#

class Generator[source]#

Bases: object

A generator is function that generates a set of values from a set of given values. Semantically, it certifies that the generated values satisfy a given condition.

Methods

short_str()

Attributes

`argument_names`	The names of the arguments of the operator.
`argument_types`	The types of the arguments of the operator.
`input_vars`	The input variables of the function.
`name`	The name of the generator.
`arguments`	The arguments of the generator.
`certifies`	The condition that the generated values should satisfy.
`context`	The context values that the generator depends on.
`generates`	The values that the generator generates.
`function`	The declaration of the underlying function that generates the values.
`output_vars`	The output variables of the function.
`output_types`	The output type of the function.
`flatten_certifies`	The condition that the generated values should satisfy, flattened.
`implementation`	The implementation of the generator.
`priority`	The priority of the generator.
`unsolvable`	Whether the generator is unsolvable.

__init__(name, arguments, certifies, context, generates, function, output_vars, flatten_certifies, implementation=None, priority=0, unsolvable=False)[source]#

Parameters:

name (str)
arguments (Sequence[Variable])
certifies (ValueOutputExpression)
context (Sequence[VariableExpression | ValueOutputExpression])
generates (Sequence[VariableExpression | ValueOutputExpression])
function (Function)
output_vars (Sequence[Variable])
flatten_certifies (ValueOutputExpression)
implementation (Implementation | None)
priority (int)
unsolvable (bool)

__new__(**kwargs)#

short_str()[source]#

property argument_names: Tuple[str, ...]#: The names of the arguments of the operator.

property argument_types: Tuple[ObjectType | ValueType, ...]#: The types of the arguments of the operator.

arguments: Tuple[Variable, ...]#: The arguments of the generator.

certifies: ValueOutputExpression#: The condition that the generated values should satisfy.

context: Tuple[VariableExpression | ValueOutputExpression, ...]#: The context values that the generator depends on.

flatten_certifies: ValueOutputExpression#: The condition that the generated values should satisfy, flattened.

function: Function#: The declaration of the underlying function that generates the values.

generates: Tuple[VariableExpression | ValueOutputExpression, ...]#: The values that the generator generates.

implementation: Implementation | None#: The implementation of the generator.

property input_vars: Tuple[Variable, ...]#: The input variables of the function.

name: str#: The name of the generator.

output_types: Tuple[TensorValueTypeBase | PyObjValueType, ...]#: The output type of the function.

output_vars: Tuple[Variable, ...]#: The output variables of the function.

priority: int#: The priority of the generator.

unsolvable: bool#: Whether the generator is unsolvable.