code: def standard_datasets(
      draw,
      dtypes=floating_dtypes(),
      n_samples=integers(min_value=0, max_value=200),
      n_features=integers(min_value=0, max_value=200),
      *,
      n_targets=just(1),
  ):
      """
      Returns a strategy to generate standard estimator input datasets.
  
      Parameters
      ----------
      dtypes: SearchStrategy[np.dtype], default=floating_dtypes()
          Returned arrays will have a dtype drawn from these types.
      n_samples: SearchStrategy[int], \
          default=integers(min_value=0, max_value=200)
          Returned arrays will have number of rows drawn from these values.
      n_features: SearchStrategy[int], \
          default=integers(min_value=0, max_values=200)
          Returned arrays will have number of columns drawn from these values.
      n_targets: SearchStrategy[int], default=just(1)
          Determines the number of targets returned datasets may contain.
  
      Returns
      -------
      X: SearchStrategy[array] (n_samples, n_features)
          The search strategy for input samples.
      y: SearchStrategy[array] (n_samples,) or (n_samples, n_targets)
          The search strategy for output samples.
  
      """
      xs = draw(n_samples)
      ys = draw(n_features)
      X = arrays(dtype=dtypes, shape=(xs, ys))
      y = arrays(dtype=dtypes, shape=(xs, draw(n_targets)))
      return draw(X), draw(y)
  
imports: from cuml.internals.array import CumlArray
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import
  from hypothesis import assume
  from hypothesis.extra.numpy import array_shapes, arrays, floating_dtypes, integer_dtypes
  from hypothesis.strategies import composite, integers, just, none, one_of, sampled_from
  from sklearn.datasets import make_classification, make_regression
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 12 for calling a global function with example usage: standard_datasets(draw, dtypes, n_samples, n_features) and returns: draw,  draw