* Computes the maximum of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims`

* Returns the max of a and b (`a > b ? a : b`) element-wise. * Supports broadcasting. * * We also expose `tf.maximumStrict` which has the same sign

* Computes the mean squared error between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions'. *

* Computes the mean of elements across dimensions of a `tf.Tensor`. * * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is * tr

* Logic for merging multiple tensors, to be overridden by subclasses. * @param inputs

* Computes the minimum value from the input. * * Reduces the input along the dimensions given in `axes`. Unless `keepDims` * is true, the rank of t

* Executes `f()` and minimizes the scalar output of `f()` by computing * gradients of y with respect to the list of trainable variables provided by

* Returns the min of a and b (`a < b ? a : b`) element-wise. * Supports broadcasting. * * We also expose `minimumStrict` which has the same signatu

@doc {heading: 'Tensors', subheading: 'Transformations'}

* Returns the mod of a and b element-wise. * `floor(x / y) * y + mod(x, y) = x` * Supports broadcasting. * * We also expose `tf.modStrict` which h

* Calculates the mean and variance of `x`. The mean and variance are * calculated by aggregating the contents of `x` across `axes`. If `x` is * 1-D

* Move a model from one URL to another. * * This function supports: * * 1. Moving within a storage medium, e.g., * `tf.io.moveModel('localstor

* Compute the moving average of a variable. * * Without zeroDebias, the moving average operation is defined by: * `v += delta` * where * `del

* Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting. * * We also expose `tf.mulStrict` which has the same signature as this op

* Computes the next states and outputs of a stack of LSTMCells. * * Each cell output is used as input to the next cell. * * Returns `[cellState, c

* Creates a `tf.Tensor` with values drawn from a multinomial distribution. * * ```js * const probs = tf.tensor([.75, .25]); * tf.multinomial(probs

* Computes `-1 * x` element-wise. * * ```js * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]); * * x.neg().print(); // or tf.neg(x) * ``` * * @p

* Converts a layer and its index to a unique (immutable type) name. * This function is used internally with `this.containerNodes`. * @param laye

Pulls a node def from a dictionary for a given name. Args: node_map: Dictionary containing an entry indexed by name for every node. name: I