code: 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)
  
imports: import os
  from .uea import _load_uea_dataset
  from .ucr import _load_ucr_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: load_coffee(return_X_y) and returns: _load_dataset