concepts.dm.pdsketch.executor.PythonFunctionRef#

class PythonFunctionRef[source]#

Bases: object

A reference to a Python function.

This class is used to wrap external function implementations in domains.

Methods

`forward`(argument_values[, return_type, ...])	Call the function.
`forward_generator`(argument_values[, ...])	Call the function and return a generator.
`forward_internal_autobatch`(function_def, ...)
`forward_sgc_function`(state, goal, ...[, ...])	Call an SGC function (state-goal-constraints) function.
`iter_from`(*args[, return_type, ...])
`set_executor`(executor)	Set the executor that is using this function reference.

Attributes

`flags`
`function`	The internal implementation of the function.
`function_quantized`	The quantized version of the function (can be None).
`return_type`	The return type of the function.
`support_batch`	Whether the function supports batched inputs.
`auto_broadcast`	Whether the executor should automatically broadcast the arguments before calling the function.
`use_object_names`	Whether the executor should use the object names in the state (instead of the index).
`unwrap_values`	Whether the executor should unwrap the tensor values before calling the function.
`include_executor_args`	Whether the caller should include the executor as the first argument.
`is_iterator`	Whether the function is an iterator.
`is_sgc_function`	Whether the function is an SGC function (state-goal-constraints).

__call__(*args, return_type=None, auto_broadcast=True, wrap_rv=True)[source]#

Call self as a function.

Parameters:

return_type (TensorValueTypeBase | PyObjValueType | None)
auto_broadcast (bool)
wrap_rv (bool)

Return type:

TensorValue | Tuple[TensorValue, …]

__init__(function, function_quantized=None, *, return_type=None, support_batch=False, auto_broadcast=False, use_object_names=True, unwrap_values=None, include_executor_args=False, is_iterator=False, is_sgc_function=False, executor=None)[source]#

Initialize a Python function reference.

Parameters:

function (Callable) – the function to be wrapped.
function_quantized (Callable | None) – the quantized version of the function (can be None).
support_batch (bool) – whether the function supports batched inputs.
auto_broadcast (bool) – whether the executor should automatically broadcast the arguments before calling the function.
use_object_names (bool) – whether the executor should use the object names in the state (instead of the index).
unwrap_values (bool | None) – whether the executor should unwrap the tensor values before calling the function.
include_executor_args (bool) – whether the caller should include the executor as the first argument.
is_iterator (bool) – whether the function is an iterator.
is_sgc_function (bool) – whether the function is an SGC function (state-goal-constraints).
executor (PDSketchExecutor | None) – the executor that is using this function reference.
return_type (TensorValueTypeBase | PyObjValueType | Tuple[TensorValueTypeBase | PyObjValueType, ...] | None)

__new__(**kwargs)#

forward(argument_values, return_type=None, additional_parameters=None, auto_broadcast=True, wrap_rv=True, function_def=None, batch_variable_names=None)[source]#

Call the function.

Parameters:

argument_values (Sequence[TensorValue | slice | StateObjectReference | ListValue | None | Tuple[TensorValue | slice | StateObjectReference | ListValue | None, ...]]) – the arguments to the function.
return_type (TensorValueTypeBase | PyObjValueType | None) – the type of the return value.
additional_parameters (Sequence[Any] | None) – the additional parameters to the function.
auto_broadcast (bool) – whether the executor should automatically broadcast the arguments before calling the function.
wrap_rv (bool) – whether the executor should wrap the return value.
function_def (Function | None) – the function definition.
batch_variable_names (Dict[int, str] | None) – the batch variable names.

Returns:

the result of the function.

Return type:

TensorValue | Tuple[TensorValue, …]

forward_generator(argument_values, return_type=None, auto_broadcast=True, wrap_rv=True)[source]#

Call the function and return a generator.

Parameters:

argument_values (Sequence[TensorValue | slice | StateObjectReference | ListValue | None | Tuple[TensorValue | slice | StateObjectReference | ListValue | None, ...]]) – the arguments to the function.
return_type (TensorValueTypeBase | PyObjValueType | None) – the type of the return value.
auto_broadcast (bool) – whether the executor should automatically broadcast the arguments before calling the function.
wrap_rv (bool) – whether the executor should wrap the

Return type:

Iterator[TensorValue] | Iterator[Tuple[TensorValue, …]]

forward_internal_autobatch(function_def, function, argument_values_flat, batch_variable_names)[source]#

forward_sgc_function(state, goal, constraints, additional_arguments, return_type=None, auto_broadcast=True, wrap_rv=True)[source]#

Call an SGC function (state-goal-constraints) function.

Parameters:

state (State) – the current state.
goal (Expression) – the goal expression.
constraints (Sequence[Expression]) – the constraints, as a list of expressions.
additional_arguments (Sequence[TensorValue | slice | StateObjectReference | ListValue | None | Tuple[TensorValue | slice | StateObjectReference | ListValue | None, ...]]) – the additional arguments.
return_type (TensorValueTypeBase | PyObjValueType | None) – the type of the return value.
auto_broadcast (bool) – whether the executor should automatically broadcast the arguments before calling the function.
wrap_rv (bool) – whether the executor should wrap the return value.

iter_from(*args, return_type=None, auto_broadcast=True, wrap_rv=True)[source]#

Parameters:

return_type (TensorValueTypeBase | PyObjValueType | None)
auto_broadcast (bool)
wrap_rv (bool)

Return type:

Iterator[TensorValue] | Iterator[Tuple[TensorValue, …]]

set_executor(executor)[source]#

Set the executor that is using this function reference.

Parameters:: executor (PDSketchExecutor) – the executor that is using this function reference.
Returns:: the function reference itself.
Return type:: PythonFunctionRef

auto_broadcast: bool#: Whether the executor should automatically broadcast the arguments before calling the function.

property flags: Dict[str, bool]#

function: Callable#: The internal implementation of the function.

function_quantized: Callable | None#: The quantized version of the function (can be None).

include_executor_args: bool#: Whether the caller should include the executor as the first argument.

is_iterator: bool#: Whether the function is an iterator.

is_sgc_function: bool#: Whether the function is an SGC function (state-goal-constraints).

return_type: TensorValueTypeBase | PyObjValueType | Tuple[TensorValueTypeBase | PyObjValueType, ...] | None#: The return type of the function.

support_batch: bool#: Whether the function supports batched inputs.

unwrap_values: bool#: Whether the executor should unwrap the tensor values before calling the function.

use_object_names: bool#: Whether the executor should use the object names in the state (instead of the index).