* A simplistic RNN step function for testing. * This step function simply * - calculates a reduced mean over all input elements, for each sample. *

* Rotates the input image tensor counter-clockwise with an optional offset * center of rotation. Currently available in the CPU, WebGL, and WASM back

* Computes round of input `tf.Tensor` element-wise: `round(x)`. * It implements banker's rounding. * * ```js * const x = tf.tensor1d([.6, 1.1, -3.

* Groups elements into batches, represented as arrays of elements. * * We can think of the elements of this iterator as 'rows' (even if they are

* Computes reciprocal of square root of the input `tf.Tensor` element-wise: * `y = 1 / sqrt(x)` * * ```js * const x = tf.tensor1d([1, 2, 4, -1]);

* Run benchmark for singular browser-device combination. * * This function utilizes a promise that is fulfilled once the corresponding * result is

* Computes output tensors for new inputs. * * Note: * - Expects `inputs` to be a list (potentially with 1 element). * * @param inputs

* Save model artifacts. This IO handler cannot support writing to the * packaged bundle at runtime and is exclusively for loading a model * that

* Save model artifacts to browser local storage. * * See the documentation to `browserLocalStorage` for details on the saved * artifacts.

r"""Save a Keras model and its weights in TensorFlow.js format. Args: model: An instance of `keras.Model`. artifacts_dir: The directory in

* Creates a new tensor by applying sparse updates to individual * values or slices within a zero tensor of the given shape tensor according to * ind

* Searches for where a value would go in a sorted sequence. * * This is not a method for checking containment (like javascript in). * * The typica

* Computes scaled exponential linear element-wise. * * `x < 0 ? scale * alpha * (exp(x) - 1) : scale * x` * * ```js * const x = tf.tensor1d([-1,

* 2-D convolution with separable filters. * * Performs a depthwise convolution that acts separately on channels followed * by a pointwise convoluti

* Force an iterator to execute serially: each next() call will await the * prior one, so that they cannot execute concurrently.

* Apply a function to every element of the stream, forcing serial execution. * * @param f A function to apply to each stream element. Should re

* Maps this stream through a 1-to-1 transform, forcing serial execution. * * @param transform A function mapping a stream element to a transform

* Set call hook. * This is currently used for testing only. * @param callHook

* Set the fast-weight-initialization flag. * * In cases where the initialized weight values will be immediately * overwritten by loaded weigh

* Sets the momentum of the optimizer. * * @param momentum

* Sets the weights of the layer, from Tensors. * * @param weights a list of Tensors. The number of arrays and their shape * must match numb

* Set the weights of the model. * * @param weights An `Array` of `tf.Tensor`s with shapes and types matching * the output of `getWeights(

* Computes the difference between two lists of numbers. * * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out` * that repre