np.inf, np.nan, pd.NA in array. The
    possibilities are:

    - True: Force all values of array to be finite.
    - False: accepts np.inf, np.nan, pd.NA in array.
    - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
      cannot be infinite.

    .. versionadded:: 0.20
       ``force_all_finite`` accepts the string ``'allow-nan'``.

    .. versionchanged:: 0.23
       Accepts `pd.NA` and converts it into `np.nan`

    .. deprecated:: 1.6
       `force_all_finite` was renamed to `ensure_all_finite` and will be removed
       in 1.8.

ensure_all_finite : bool or 'allow-nan', default=True
    Whether to raise an error on np.inf, np.nan, pd.NA in array. The
    possibilities are:

    - True: Force all values of array to be finite.
    - False: accepts np.inf, np.nan, pd.NA in array.
    - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
      cannot be infinite.

    .. versionadded:: 1.6
       `force_all_finite` was renamed to `ensure_all_finite`.

ensure_non_negative : bool, default=False
    Make sure the array has only non-negative values. If True, an array that
    contains negative values will raise a ValueError.

    .. versionadded:: 1.6

ensure_2d : bool, default=True
    Whether to raise a value error if array is not 2D.

allow_nd : bool, default=False
    Whether to allow array.ndim > 2.

ensure_min_samples : int, default=1
    Make sure that the array has a minimum number of samples in its first
    axis (rows for a 2D array). Setting to 0 disables this check.

ensure_min_features : int, default=1
    Make sure that the 2D array has some minimum number of features
    (columns). The default value of 1 rejects empty datasets.
    This check is only enforced when the input data has effectively 2
    dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
    disables this check.

estimator : str or estimator instance, default=None
    If passed, include the name of the estimator in warning messages.

input_name : str, default=""
    The data name used to construct the error message. In particular
    if `input_name` is "X" and the data has NaN values and
    allow_nan is False, the error message will link to the imputer
    documentation.

    .. versionadded:: 1.1.0

Returns
-------
array_converted : object
    The converted and validated array.

Examples
--------
>>> from sklearn.utils.validation import check_array
>>> X = [[1, 2, 3], [4, 5, 6]]
>>> X_checked = check_array(X)
>>> X_checked
array([[1, 2, 3], [4, 5, 6]])
z©np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.htmlrú