code: def test_mida(kernel, augmentation):
      np.random.seed(29118)
      # Generate toy data
      n_samples = 200
  
      xs, ys = make_blobs(
          n_samples,
          n_features=3,
          centers=[[0, 0, 0], [0, 2, 1]],
          cluster_std=[0.3, 0.35],
      )
      xt, yt = make_blobs(
          n_samples,
          n_features=3,
          centers=[[2, -2, 2], [2, 0.2, -1]],
          cluster_std=[0.35, 0.4],
      )
      x = np.concatenate((xs, xt), axis=0)
  
      covariates = np.zeros(n_samples * 2)
      covariates[:n_samples] = 1
  
      enc = OneHotEncoder(handle_unknown="ignore")
      covariates_mat = enc.fit_transform(
          covariates.reshape(-1, 1)
      ).toarray()
      mida = MIDA(
          n_components=2, kernel=kernel, augmentation=augmentation
      )
      x_transformed = mida.fit_transform(x, covariates=covariates_mat)
      testing.assert_equal(x_transformed.shape, (n_samples * 2, 2))
  
      x_transformed = mida.fit_transform(
          x, ys, covariates=covariates_mat
      )
      testing.assert_equal(x_transformed.shape, (n_samples * 2, 2))
  
file_path: pykale/tests/embed/test_factorization.py
imports: import os
  import numpy
  import pytest
  from numpy import testing
  from scipy.io import loadmat
  from sklearn.datasets import make_blobs
  from sklearn.preprocessing import OneHotEncoder
  from tensorly.tenalg import multi_mode_dot
  from kale.embed.factorization import MIDA, MPCA
label_desc: Use image node_id 4 for calling a global function with example usage: test_mida(kernel, augmentation) without return types