* Casts a `tf.Tensor` to a new dtype. * * ```js * const x = tf.tensor1d([1.5, 2.5, 3]); * tf.cast(x, 'int32').print(); * ``` * @param x The inpu

* Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)` * * ```js * const x = tf.tensor1d([.6, 1.1, -3.3]); * * x.ceil().print(); // or

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Clean up the graph def by removing the skipped nodes and clean up the nodes with inputs that have been removed. Args: input_graph_def: Grap

* Dispose the tensors and idTensor and clear the tensor list.

* Dispose the tensors and idTensor and mark the TensoryArray as closed.

* Clear call hook. * This is currently used for testing only.

* Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.clipByValue(-2, 3

* Returns a copy of the tensor. See `tf.clone` for details. * @doc {heading: 'Tensors', subheading: 'Classes'}

* Creates a new tensor with the same values and shape as the specified * tensor. * * ```js * const x = tf.tensor([1, 2]); * * x.clone().print();

* Groups elements into batches, represented in column-major form. * * We can think of the elements of this iterator as 'rows' (even if they are

* Converts two real numbers to a complex number. * * Given a tensor `real` representing the real part of a complex number, and a * tensor `imag` re

* If a getRunner function is present in props, execute it * numRun times and report the result and timing info.

* If a getRunner function is present in props, execute it * numRun times and report the result and timing info.

* Computes the shape of the result of an elementwise operation. * * @param shape1: Shape of the first tensor. * @param shape2: Shape of the s

* Executes f() and computes the gradient of the scalar output of f() with * respect to the list of trainable variables provided by `varList`. If no

* Computes an output mask tensor. * * @param inputs Tensor or list of tensors. * @param mask Tensor or list of tensors. * * @return nul

* Computes an output mask tensor. * * @param inputs Tensor or list of tensors. * @param mask Tensor or list of tensors. * * @return nul

* Computes the output shape of the layer. * * Assumes that the layer will be built to match that input shape provided. * * @param inputSha

* Computes the output shape of the layer. * * Assumes that the layer will be built to match that input shape provided. * * @param inputSha