code: def test_one_many_consistent():
      """Checks that using learn_one or learn_many produces the same result."""
  
      X = pd.read_csv(datasets.TrumpApproval().path)
      Y = X.pop("five_thirty_eight")
  
      one = lm.LinearRegression()
      for x, y in stream.iter_pandas(X[:100], Y[:100]):
          one.learn_one(x, y)
  
      many = lm.LinearRegression()
      for xb, yb in zip(
          np.array_split(X[:100], len(X[:100])),
          np.array_split(Y[:100], len(Y[:100])),
      ):
          many.learn_many(xb, yb)
  
      for i in X:
          assert math.isclose(one.weights[i], many.weights[i])
  
imports: from __future__ import annotations
  import copy
  import itertools
  import math
  import random
  import numpy
  import pandas
  import pytest
  from sklearn import linear_model
  from sklearn.datasets import make_classification, make_regression
  from sklearn.metrics import log_loss
  from river import datasets, optim, preprocessing, stream, utils
  from river import linear_model
label_desc: Use image node_id 3 for calling a global function with example usage: test_one_many_consistent() without return types