function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
    x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "norm");
    const norm = normImpl(x, ord, axis);
    let keepDimsShape = norm.shape;
    if (keepDims) {
        const axes = (0, _util.parseAxisParam)(axis, x.shape);
        keepDimsShape = _axisUtil.expandShapeToKeepDim(norm.shape, axes);
    }
    return (0, _reshape.reshape)(norm, keepDimsShape);
}
Computes the norm of scalar, vectors, and matrices.
This function can compute several different vector norms (the 1-norm, the
Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)
and matrix norms (Frobenius, 1-norm, and inf-norm).

```js
const x = tf.tensor1d([1, 2, 3, 4]);

x.norm().print();  // or tf.norm(x)
```

@param x The input array.
@param ord Optional. Order of the norm. Supported norm types are
following:

 | ord        | norm for matrices         | norm for vectors
 |------------|---------------------------|---------------------
 |'euclidean' |Frobenius norm             |2-norm
 |'fro'       |Frobenius norm	           |
 |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))
 |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))
 |1           |max(sum(abs(x), axis=0))   |sum(abs(x))
 |2           |                           |sum(abs(x)^2)^(1/2)

@param axis Optional. If axis is null (the default), the input is
considered a vector and a single vector norm is computed over the entire
set of values in the Tensor, i.e. norm(x, ord) is equivalent
to norm(x.reshape([-1]), ord). If axis is an integer, the input
is considered a batch of vectors, and axis determines the axis in x
over which to compute vector norms. If axis is a 2-tuple of integer it is
considered a batch of matrices and axis determines the axes in NDArray
over which to compute a matrix norm.
@param keepDims Optional. If true, the norm has the same dimensionality
as the input.

@doc {heading: 'Operations', subheading: 'Matrices'}