class_docstr: Scales a set of features so that it has unit norm.
  
  This is particularly useful when used after a `feature_extraction.TFIDF`.
  
  Parameters
  ----------
  order
      Order of the norm (e.g. 2 corresponds to the $L^2$ norm).
  
  Examples
  --------
  
  >>> from river import preprocessing
  >>> from river import stream
  
  >>> scaler = preprocessing.Normalizer(order=2)
  
  >>> X = [[4, 1, 2, 2],
  ...      [1, 3, 9, 3],
  ...      [5, 7, 5, 1]]
  
  >>> for x, _ in stream.iter_array(X):
  ...     print(scaler.transform_one(x))
  {0: 0.8, 1: 0.2, 2: 0.4, 3: 0.4}
  {0: 0.1, 1: 0.3, 2: 0.9, 3: 0.3}
  {0: 0.5, 1: 0.7, 2: 0.5, 3: 0.1}
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code: def transform_one(self, x):
      norm = utils.math.norm(x, order=self.order)
      return {i: xi / norm for i, xi in x.items()}
  
imports: from __future__ import annotations
  import collections
  import functools
  import itertools
  import numbers
  import numpy
  import pandas
  from river import base, stats, utils
label_desc: Use image node_id 2 for calling the Normalizer obj's underlying member method code with example usage: obj.transform_one(x) and returns: unknown