code: def test_infer_dim_1():
      # TODO: explain what this is testing
      # Or at least use explicit variable names...
      n, p = 1000, 5
      rng = np.random.RandomState(0)
      X = (
          rng.randn(n, p) * 0.1
          + rng.randn(n, 1) * np.array([3, 4, 5, 1, 2])
          + np.array([1, 0, 7, 4, 6])
      )
      X = da.from_array(X, chunks=(n, p))
      pca = dd.PCA(n_components=p, svd_solver="full")
      pca.fit(X)
  
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 17 for calling a global function with example usage: test_infer_dim_1() without return types