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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
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. |