code: def test_transformed_shape(self):
      # checks if the transformed objects have the correct columns
      a = dpp.PolynomialFeatures()
      a.fit(X)
      n_cols = len(a.get_feature_names_out())
      # dask array
      assert a.transform(X).shape[1] == n_cols
      # numpy array
      assert a.transform(X.compute()).shape[1] == n_cols
      X_nan_rows = df.values
      # dask array with nan rows
      assert a.transform(X_nan_rows).shape[1] == n_cols
  
imports: from copy import copy
  import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pandas.testing
  import pytest
  import sklearn.preprocessing
  from dask import compute
  from dask.array.utils import assert_eq
  from dask.dataframe.utils import assert_eq
  from pandas.api.types import is_categorical_dtype, is_object_dtype
  from sklearn.exceptions import NotFittedError
  import dask_ml.preprocessing
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_estimator_equal
label_desc: Use image node_id 5 for calling the TestPolynomialFeatures obj's underlying member method code with example usage: obj.test_transformed_shape() without return types