Returns the data type.

r'''Get singleton.

Returns the result of the Metric. Args: write_summary: bool indicating whether to feed the result to the summary before returning.

Make and run an experiment. It creates an Experiment by calling `experiment_fn`. Then it calls the function named as `schedule` of the Experiment

Note that it is not required that the first dimension be the number of batches. But it is required that the last dimension is the 'input_dim'. For th

Set the string values of an attribute of the operation being built. @param name attribute name @param value attribute values @return the OperationBui

Fit the estimator using the training data. Train the estimator for `self._train_steps` steps, after waiting for `delay_secs` seconds. If `sel

Returns the total count of elements, that is the size when flattened into a vector.

Precondition: !IsUninitialized(). It's very common to do foo.builder()->bar(). Without this precondition, if foo.builder() is null, the call to bar

Returns the resource manager associated w/ this device.

static */

Return new `LinearOperator` acting like `op1 + op2`. Args: op1: `LinearOperator` op2: `LinearOperator`, with `shape` and `dtype` su

Call `metric_value_fn` in the correct control flow context.

Matches a node with op `op`.

Adds a new variable to the layer. Arguments: name: Variable name. shape: Variable shape. Defaults to scalar if unspecified. dty

Shape of a single sample from a single batch as a 1-D int32 `Tensor`. Args: name: name to give to the op Returns: event_shape: `

If you know how many elements you will push at the time you create the TopN object, you can call reserve to preallocate the memory that TopN will need

Sequence of variables owned by this module and it's submodules. Note: this method uses reflection to find variables on the current instance a

Stores `value` in the collections given by `names`. Note that collections are not sets, so it is possible to add a value to a collection seve

Store export information for constants/string literals. Export information is stored in the module where constants/string literals are define

Returns a new `tf.data.Dataset` with the given options set. The options are "global" in the sense they apply to the entire dataset. If option

Return histogram of values. Given the tensor `values`, this operation returns a rank 1 histogram counting the number of entries in `values` t

Push 'v'. If the maximum number of elements was exceeded, drop the lowest element and return it in 'dropped' (if given). If the maximum is not exceed

Gets one element from this staging area. If the staging area is empty when this operation executes, it will block until there is an element t

Computes the given reduction across the given axes of a LabeledTensor. See `tf.{op_name}` for full details. Args: labeled_tensor: The

Assert that actual.startswith(expected_start) is True. Args: actual: str expected_start: str msg: Optional message to report on

Returns a `Tensor` for the given key. A `str` key is used to access a base feature (not-transformed). When a `_FeatureColumn` is passed, the

Return a slot named `name` created for `var` by the Optimizer. Some `Optimizer` subclasses use additional variables. For example `Momentum`

Overload to call And with 3 or more operands. We need the following somewhat convoluted overload set to disambiguate with the overload that takes the

Enqueues a constant with the value of the given literal onto the computation.

Returns the list of colocation groups of the op.

Gets the computations in this module. Returns a view of HloComputation*s, so you can iterate over this in the natural way: for (HloComputation* c :

Set up argument parser, and parse CLI args.

Helper to create a WrappedTensor object.

Apply gradients to variables. This is the second part of `minimize()`. It returns an `Operation` that applies gradients. Args: gra

Blocks until the thread terminates. Raises: self._testcase.failureException: If the thread terminates with due to an exceptio

Updates the shape of this tensor. This method can be called multiple times, and will merge the given `shape` with the current shape of this t

Helper to make a zip file of a bunch of TensorFlow models. This does a cartestian product of the dictionary of test_parameters and calls make_gra

Removes the registered function corresponding to `token`.