def load_coffee(return_X_y=False):
    r"""Load and return the Coffee dataset.

    Food spectrographs are used in chemometrics to classify food types, a task
    that has obvious applications in food safety and quality assurance. The
    coffee data set is a two class problem to distinguish between Robusta and
    Aribica coffee beans.

    ================   ==============
    Training samples               28
    Test samples                   28
    Timestamps                    286
    Classes                         2
    ================   ==============

    Parameters
    ----------
    return_X_y : bool (default = False)
        If True, return
        ``(data_train, data_test, target_train, target_test)`` instead of a
        Bunch object.

    Returns
    -------
    data : Bunch
        Dictionary-like object, with attributes:

        data_train : array of floats
            The time series in the training set.
        data_test : array of floats
            The time series in the test set.
        target_train : array of integers
            The classification labels in the training set.
        target_test : array of integers
            The classification labels in the test set.
        DESCR : str
            The full description of the dataset.
        url : str
            The url of the dataset.

    (data_train, data_test, target_train, target_test) : tuple if ``return_X_y`` is True

    References
    ----------
    .. [1] R. Briandet, E.K. Kemsley, and R.H. Wilson, "Discrimination of
           Arabica and Robusta in Instant Coffee by Fourier Transform Infrared
           Spectroscopy and Chemometrics". Journal of Agricultural and Food
           Chemistry (1996).

    .. [2] A. Bagnall, L. Davis, J. Hills and J. Lines, "Transformation Based
           Ensembles for Time Series Classification". SDM (2012).

    .. [3] `UCR archive entry for the PigCVP dataset
           <http://www.timeseriesclassification.com/description.php?Dataset=Coffee>`_

    Examples
    --------
    >>> from pyts.datasets import load_coffee
    >>> bunch = load_coffee()
    >>> bunch.data_train.shape
    (28, 286)
    >>> X_train, X_test, y_train, y_test = load_coffee(return_X_y=True)
    >>> X_train.shape
    (28, 286)

    """  # noqa: E501
    return _load_dataset('Coffee', 'UCR', return_X_y)
Load and return the Coffee dataset.

    Food spectrographs are used in chemometrics to classify food types, a task
    that has obvious applications in food safety and quality assurance. The
    coffee data set is a two class problem to distinguish between Robusta and
    Aribica coffee beans.

    ================   ==============
    Training samples               28
    Test samples                   28
    Timestamps                    286
    Classes                         2
    ================   ==============

    Parameters
    ----------
    return_X_y : bool (default = False)
        If True, return
        ``(data_train, data_test, target_train, target_test)`` instead of a
        Bunch object.

    Returns
    -------
    data : Bunch
        Dictionary-like object, with attributes:

        data_train : array of floats
            The time series in the training set.
        data_test : array of floats
            The time series in the test set.
        target_train : array of integers
            The classification labels in the training set.
        target_test : array of integers
            The classification labels in the test set.
        DESCR : str
            The full description of the dataset.
        url : str
            The url of the dataset.

    (data_train, data_test, target_train, target_test) : tuple if ``return_X_y`` is True

    References
    ----------
    .. [1] R. Briandet, E.K. Kemsley, and R.H. Wilson, "Discrimination of
           Arabica and Robusta in Instant Coffee by Fourier Transform Infrared
           Spectroscopy and Chemometrics". Journal of Agricultural and Food
           Chemistry (1996).

    .. [2] A. Bagnall, L. Davis, J. Hills and J. Lines, "Transformation Based
           Ensembles for Time Series Classification". SDM (2012).

    .. [3] `UCR archive entry for the PigCVP dataset
           <http://www.timeseriesclassification.com/description.php?Dataset=Coffee>`_

    Examples
    --------
    >>> from pyts.datasets import load_coffee
    >>> bunch = load_coffee()
    >>> bunch.data_train.shape
    (28, 286)
    >>> X_train, X_test, y_train, y_test = load_coffee(return_X_y=True)
    >>> X_train.shape
    (28, 286)

    
pyts/datasets/load.py