code: def test_TuckerRegressor():
      """Test for TuckerRegressor"""
  
      # Parameter of the experiment
      image_height = 10
      image_width = 10
      n_channels = 3
      ranks = [5, 5, 2]
      tol = 0.05
  
      # Generate random samples
      X = T.tensor(
          np.random.normal(
              size=(1200, image_height, image_width, n_channels),
              loc=0,
              scale=1,
          )
      )
      regression_weights = np.zeros(
          (image_height, image_width, n_channels)
      )
      regression_weights[2:-2, 2:-2, 0] = 1
      regression_weights[2:-2, 2:-2, 1] = 2
      regression_weights[2:-2, 2:-2, 2] = -1
      regression_weights = T.tensor(regression_weights)
  
      y = T.dot(
          partial_tensor_to_vec(X, skip_begin=1),
          tensor_to_vec(regression_weights),
      )
      X_train = X[:1000, :, :]
      X_test = X[1000:, :, :]
      y_train = y[:1000]
      y_test = y[1000:]
  
      estimator = TuckerRegressor(
          weight_ranks=ranks,
          tol=10e-8,
          reg_W=1,
          n_iter_max=200,
          verbose=True,
      )
      estimator.fit(X_train, y_train)
      y_pred = estimator.predict(X_test)
      error = RMSE(y_test, y_pred)
      assert_(
          error <= tol, msg=f"Tucker Regression : RMSE={error} > {tol}"
      )
  
      params = estimator.get_params()
      assert_(
          params["weight_ranks"] == [5, 5, 2],
          msg="get_params did not return the correct parameters",
      )
      params["weight_ranks"] = [5, 5, 5]
      estimator.set_params(**params)
      assert_(
          estimator.weight_ranks == [5, 5, 5],
          msg="set_params did not correctly set the given parameters",
      )
  
imports: import numpy
  from ..tucker_regression import TuckerRegressor
  from ...base import tensor_to_vec, partial_tensor_to_vec
  from ...metrics.regression import RMSE
  from ...None import backend
  from ...testing import assert_
label_desc: Use image node_id 1 for calling a global function with example usage: test_TuckerRegressor() without return types