code: def _fit_model_pls_regressor(self, model):
      X = [
          [0.0, 0.0, 1.0],
          [1.0, 0.0, 0.0],
          [2.0, 2.0, 2.0],
          [2.0, 5.0, 4.0],
      ]
      Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
      model.fit(X, Y)
  
      torch_model = hummingbird.ml.convert(model, "torch")
      self.assertTrue(torch_model is not None)
      np.testing.assert_allclose(
          model.predict(X),
          torch_model.predict(X),
          rtol=1e-6,
          atol=2 * 1e-5,
      )
  
imports: import unittest
  import warnings
  import sys
  from packaging.version import Version, parse
  import numpy
  import torch
  import sklearn
  from sklearn.decomposition import FastICA, KernelPCA, PCA, TruncatedSVD
  from sklearn.cross_decomposition import PLSRegression
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import load_digits
  import hummingbird.ml
label_desc: Use image node_id 1 for calling the TestSklearnCrossDecomposition obj's underlying member method code with example usage: obj._fit_model_pls_regressor(model) without return types