Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

Enter

public final class Enter

Creates or finds a child frame, and makes `data` available to the child frame.

This op is used together with `Exit` to create loops in the graph. The unique `frame_name` is used by the `Executor` to identify frames. If `is_constant` is true, `output` is a constant in the child frame; otherwise it may be changed in the child frame. At most `parallel_iterations` iterations are run in parallel in the child frame.

Nested Classes

class Enter.Options Optional attributes for Enter

Public Methods

Output <T>
asOutput ()
Returns the symbolic handle of a tensor.
static <T> Enter <T>
create ( Scope scope, Operand <T> data, String frameName, Options... options)
Factory method to create a class wrapping a new Enter operation.
static Enter.Options
isConstant (Boolean isConstant)
Output <T>
output ()
The same tensor as `data`.
static Enter.Options
parallelIterations (Long parallelIterations)

Inherited Methods

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static Enter <T> create ( Scope scope, Operand <T> data, String frameName, Options... options)

Factory method to create a class wrapping a new Enter operation.

Parameters
scope current scope
data The tensor to be made available to the child frame.
frameName The name of the child frame.
options carries optional attributes values
Returns
  • a new instance of Enter

public static Enter.Options isConstant (Boolean isConstant)

Parameters
isConstant If true, the output is constant within the child frame.

public Output <T> output ()

The same tensor as `data`.

public static Enter.Options parallelIterations (Long parallelIterations)

Parameters
parallelIterations The number of iterations allowed to run in parallel.