* Adds an item to the end of the buffer.

* Concatenate a number of ArrayBuffers into one. * * @param buffers An array of ArrayBuffers to concatenate, or a single * ArrayBuffer.

* Write a scalar summary. * * @param name A name of the summary. The summary tag for TensorBoard will be * this name. * @param value A r

* Returns the value in the buffer at the provided location. * * @param locs The location indices. * * @doc {heading: 'Tensors', subheading

* Returns the tensor data as a nested array. The transfer of data is done * asynchronously. * * @doc {heading: 'Tensors', subheading: 'Classe

* Sets a value in the buffer at a given location. * * @param value The value to set. * @param locs The location indices. * * @doc {hea

* Execute the inference for the input tensors. * * @param input The input tensors, when there is single input for the model, * inputs param s

Returns number of data ids currently in the storage.

* Execute a kernel with the given name and return the output tensor. * * @param kernelName The name of the kernel to execute. * @param inputs

* Calculate the activation function. * * @param x: Input. * @param alpha: Scaling factor the negative section. * @return Output of the ELU

Matches the string with the pattern, caching the compiled regexp.

Searches the string for the pattern, caching the compiled regexp.

* Configures and prepares the model for training and evaluation. Compiling * outfits the model with an optimizer, loss, and/or metrics. Calling `

* Create a data bucket in cpu backend. * @param shape Shape of the `TensorInfo`. * @param dtype DType of the `TensorInfo`. * @param values Th

* Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have * the same shape and dtype as the old `tf.Tensor`. * * @param newV

* Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`, * which gives an array of gradients of `f()` with respect to each input

* Executes a TensorFlow Eager Op that provides one output Tensor. * @param name The name of the Op to execute. * @param opAttrs The list of Op a

* Disposes `tf.Tensor` from memory. * * @doc {heading: 'Tensors', subheading: 'Classes'}

* Make a tensor info for the output of an op. If `memoryOffset` is not * present, this method allocates memory on the WASM heap. If `memoryOffset`

* Delete the SavedModel from nodeBackend and delete corresponding session in * the C++ backend if the session is only used by this TFSavedModel.

* Returns a human-readable description of the tensor. Useful for logging. * * @doc {heading: 'Tensors', subheading: 'Classes'}