code: def make_data(
      num_examples=200,
      num_features=3,
      noise=0.2,
      error_frac=0.1,
      error_noise=5,
  ):
      X = np.random.random(size=(num_examples, num_features))
      coefficients = np.random.uniform(-1, 1, size=num_features)
      label_noise = np.random.normal(scale=noise, size=num_examples)
  
      true_y = np.dot(X, coefficients)
      y = np.dot(X, coefficients) + label_noise
  
      # add extra noisy examples
      num_errors = int(num_examples * error_frac)
      extra_noise = np.random.normal(scale=error_noise, size=num_errors)
      random_idx = np.random.choice(num_examples, num_errors)
      y[random_idx] += extra_noise
      error_idx = np.argsort(abs(y - true_y))[
          -num_errors:
      ]  # get the noisiest examples idx
  
      # create test set
      X_test = np.random.random(size=(num_examples, num_features))
      label_noise = np.random.normal(scale=noise, size=num_examples)
      y_test = np.dot(X_test, coefficients) + label_noise
  
      return {
          "X": X,
          "y": y,
          "true_y": true_y,
          "X_test": X_test,
          "y_test": y_test,
          "error_idx": error_idx,
      }
  
imports: import pytest
  import random
  import numpy
  import pandas
  from sklearn.svm import SVR
  from sklearn.metrics import r2_score
  from sklearn.linear_model import LinearRegression
  from cleanlab.regression.rank import get_label_quality_scores, _get_residual_score_for_each_label, _get_outre_score_for_each_label
  from cleanlab.regression.learn import CleanLearning
label_desc: Use image node_id 1 for calling a global function with example usage: make_data(num_examples, num_features, noise, error_frac, error_noise) and returns: dict