code: def synthetic_linearly_separable_data(
      num_train: int, num_test: int, dimension: int, num_classes: int
  ) -> Tuple[RegressionDataset, RegressionDataset]:
      """Generates synthetic train and test data for logistic regression.
  
      Args:
        num_train: number of training data points.
        num_test: number of test data points.
        dimension: the dimension of the classification problem.
        num_classes: number of classes, assumed to be at least 2.
  
      Returns:
        train_dataset: num_train labeled examples, with unit l2-norm points.
        test_dataset: num_test labeled examples, with unit l2-norm points.
      """
      if num_classes < 2:
          raise ValueError(
              f"num_classes must be at least 2. It is {num_classes}."
          )
  
      # Generate weight vector.
      weights = np.random.normal(size=(dimension, num_classes))
  
      # Generate train labeled examples.
      train_dataset = linearly_separable_labeled_examples(
          num_train, weights
      )
  
      # Generate test labeled examples.
      test_dataset = linearly_separable_labeled_examples(
          num_test, weights
      )
  
      return (train_dataset, test_dataset)
  
file_path: privacy/tensorflow_privacy/privacy/logistic_regression/datasets.py
imports: import dataclasses
  from typing import Tuple, Optional
  import numpy
  from sklearn import preprocessing
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: synthetic_linearly_separable_data(num_train, num_test, dimension, num_classes) and returns: train_dataset,  test_dataset