code: def test_pca_sklearn_inputs(input_type, solver):
      Y = input_type(X)
  
      a = dd.PCA()
      with pytest.raises(TypeError, match="unsupported type"):
          a.fit(Y)
      with pytest.raises(TypeError, match="unsupported type"):
          a.fit_transform(Y)
  
imports: from itertools import product
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import scipy
  import sklearn.decomposition
  from dask.array.utils import assert_eq
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_raises
  from sklearn import datasets
  from sklearn.utils import check_random_state
  import dask_ml.decomposition
  from dask_ml._compat import DASK_2_26_0
  from dask_ml.utils import assert_estimator_equal, flip_vector_signs, svd_flip
label_desc: Use image node_id 32 for calling a global function with example usage: test_pca_sklearn_inputs(input_type, solver) without return types