tf.types.experimental.AtomicFunction
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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
View source
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__
View source
__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.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.types.experimental.AtomicFunction\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/types/core.py#L142-L166) |\n\nBase class for graph functions.\n\nInherits From: [`Callable`](../../../tf/types/experimental/Callable)\n\nAn `AtomicFunction` encapsulates a single graph function definition.\n\n`AtomicFunction` can be called directly only if no captures are needed\naccording to the `FunctionType`. If captures are present, please use\n`call_with_captures` instead.\n\n`AtomicFunction` does not support gradients. Please use the parent\n`ConcreteFunction` if you need gradient support.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-----------------|--------------------------------------------------|\n| `function_type` | Returns a FunctionType describing this callable. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `call_with_captures`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/types/core.py#L156-L166) \n\n call_with_captures(\n args, kwargs, captures\n )\n\nCalls this AtomicFunction with captures as defined by its FunctionType.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------|----------------------------------------------------|\n| `args` | Tuple containing positional arguments |\n| `kwargs` | Dict containing keyword arguments |\n| `captures` | Tuple of tensors supplying captured tensor values. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A structured output value based on the inputs. ||\n\n\u003cbr /\u003e\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/types/core.py#L124-L139) \n\n __call__(\n *args, **kwargs\n )\n\nExecutes this callable.\n\nThis behaves like a regular op - in eager mode, it immediately starts\nexecution, returning results. In graph mode, it creates ops which return\nsymbolic TensorFlow values (like [`tf.Tensor`](../../../tf/Tensor), [`tf.data.Dataset`](../../../tf/data/Dataset),\netc.). For example, [`tf.function`](../../../tf/function) callables typically generate a\n[`tf.raw_ops.PartitionedCall`](../../../tf/raw_ops/PartitionedCall) op, but not always - the\nexact operations being generated are an internal implementation detail.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------|-----------------------------------|\n| `*args` | positional argument for this call |\n| `**kwargs` | keyword arguments for this call |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| The execution results. ||\n\n\u003cbr /\u003e"]]
View source on GitHub