Returns a unique temporary directory for the test to use. If you call this method multiple times during in a test, it will return the same fo

Evaluates given model with provided evaluation data. Stop conditions - we evaluate on the given input data until one of the following: -

Returns a TensorFlow Session for use in executing tests. Note that this will set this session and the graph as global defaults. Use the `use

Gets an existing variable with this name or create a new one.

static */

Performs a sum reduction on `x` across pfor iterations. Note that this currently may not work inside a control flow construct. Args: x:

Minimum value in a tensor. Arguments: x: A tensor or variable. axis: An integer, the axis to find minimum values. keepdims: A boo

Configures the model for training. Arguments: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.opti

Add a wrapped input argument to the hint. Args: *args: The input tensor. **kwargs: "name" label "tag" a tag to group

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

Returns a numpy array or a scalar with the same contents as the Tensor. TODO(ashankar,agarwal): Perhaps this should NOT reference the underlying

Group tensors together. This creates a tuple of tensors with the same values as the `tensors` argument, except that the value of each tensor is o

Return a pointer to the entry computation of the module.

Returns the type of the operation, i.e., the name of the computation performed by the operation.

All of Input's constructors are implicit. Input can be implicitly constructed from the following objects : Output: This is so that the output of an Op

Converts a float value into an unsigned eight-bit quantized value.

Creates a `Dataset` of a step-separated range of values. For example: ```python Dataset.range(5) == [0, 1, 2, 3, 4] Dataset.range(2,

Append an item to the Menu. Args: item: (MenuItem) the item to be appended.

Returns a list of values in the collection with the given `name`. This is different from `get_collection_ref()` which always returns the actu

Returns a TensorArray with the same content and properties. Returns: A new TensorArray object with flow that ensures the control dependenci

Use TF logging for TensorRT informations

Returns the rank (number of dimensions) of the given shape. Shape must be an array.

- Parameter(N) matches parameter number N. - Parameter() matches any parameter.

Returns a context manager that specifies the default device to use. The `device_name_or_function` argument may either be a device name string

Alias of `Variable.shape`.

Returns a const iterator pointing to the past-the-end wrapped tensor input.

Trains the model for a fixed number of epochs (iterations on a dataset). Arguments: x: Input data. It could be: - A Numpy array

Assigns a new value to the variable. This is essentially a shortcut for `assign(self, value)`. Args: value: A `Tensor`. The new value

Wrapper around Session.run() that inserts tensor watch options. Args: fetches: Same as the `fetches` arg to regular `Session.run()`.

Warning: note that the pointer returned is not necessarily directly to device virtual address space, but is platform-dependent.

Split the values of a `Tensor` into the TensorArray. Args: value: (N+1)-D. Tensor of type `dtype`. The Tensor to split. lengths: 1-

Download and parse MNIST dataset.