tf.types.experimental.AtomicFunction
Base class for graph functions.
Inherits From: Callable
An AtomicFunction encapsulates a single graph function definition.
AtomicFunction can be called directly only if no captures are needed according to the FunctionType. If captures are present, please use call_with_captures instead.
AtomicFunction does not support gradients. Please use the parent ConcreteFunction if you need gradient support.
| Attributes | |
|---|---|
function_type | Returns a FunctionType describing this callable. |
Methods
call_with_captures
call_with_captures(
args, kwargs, captures
)
Calls this AtomicFunction with captures as defined by its FunctionType.
| Args | |
|---|---|
args | Tuple containing positional arguments |
kwargs | Dict containing keyword arguments |
captures | Tuple of tensors supplying captured tensor values. |
| Returns | |
|---|---|
| A structured output value based on the inputs. |
__call__
__call__(
*args, **kwargs
)
Executes this callable.
This behaves like a regular op - in eager mode, it immediately starts execution, returning results. In graph mode, it creates ops which return symbolic TensorFlow values (like tf.Tensor, tf.data.Dataset, etc.). For example, tf.function callables typically generate a tf.raw_ops.PartitionedCall op, but not always - the exact operations being generated are an internal implementation detail.
| Args | |
|---|---|
*args | positional argument for this call |
**kwargs | keyword arguments for this call |
| Returns | |
|---|---|
| The execution results. |
© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/types/experimental/AtomicFunction
