human
Complete the below
@verbose
def compute_covariance(epochs, keep_sample_mean=True, tmin=None, tmax=None, projs=None, method='empirical', method_params=None, cv=3, scalings=None, n_jobs=1, return_estimators=False, on_mismatch='raise', verbose=None):

    ''''Estimate noise covariance matrix from epochs.
    The noise covariance is typically estimated on pre-stim periods
    when the stim onset is defined from events.
    If the covariance is computed for multiple event types (events
    with different IDs), the following two options can be used and combined:
    1. either an Epochs object for each event type is created and
    a list of Epochs is passed to this function.
    2. an Epochs object is created for multiple events and passed
    to this function.
    .. note:: Baseline correction should be used when creating the Epochs.
    Otherwise the computed covariance matrix will be inaccurate.
    .. note:: For multiple event types, it is also possible to create a
    single Epochs object with events obtained using
    merge_events(). However, the resulting covariance matrix
    will only be correct if keep_sample_mean is True.
    .. note:: The covariance can be unstable if the number of samples is
    not sufficient. In that case it is common to regularize a
    covariance estimate. The ``method`` parameter of this
    function allows to regularize the covariance in an
    automated way. It also allows to select between different
    alternative estimation algorithms which themselves achieve
    regularization. Details are described in [1]_.
    Parameters
    epochs : instance of Epochs, or a list of Epochs objects
    The epochs.
    keep_sample_mean : bool (default True)
    If False, the average response over epochs is computed for
    each event type and subtracted during the covariance
    computation. This is useful if the evoked response from a
    previous stimulus extends into the baseline period of the next.
    Note. This option is only implemented for method=\'empirical\'.
    tmin : float | None (default None)
    Start time for baseline. If None start at first sample.
    tmax : float | None (default None)
    End time for baseline. If None end at last sample.
    projs : list of Projection | None (default None)
    List of projectors to use in...'''

=====
Write me a the Python program you requested?
assistant
#Here is the Python program you requested.
    accepted_methods = ('auto', 'empirical', 'diagonal_fixed', 'ledoit_wolf', 'shrunk', 'pca', 'factor_analysis')
    msg = ('Invalid    method    ({method}).    Accepted    values    (individually    or    in    a    list)    are    "%s"    or    None.' % '"    or    "'.join(accepted_methods))
    scalings = _check_scalings_user(scalings)
    _method_params = {'empirical': {'store_precision': False, 'assume_centered': True}, 'diagonal_fixed': {'grad': 0.01, 'mag': 0.01, 'eeg': 0.0, 'store_precision': False, 'assume_centered': True}, 'ledoit_wolf': {'store_precision': False, 'assume_centered': True}, 'shrunk': {'shrinkage': np.logspace((-4), 0, 30), 'store_precision': False, 'assume_centered': True}, 'pca': {'iter_n_components': None}, 'factor_analysis': {'iter_n_components': None}}
    if isinstance(method_params, dict):
        for (key, values) in method_params.items():
            if (key not in _method_params):
                raise ValueError(('key    (%s)    must    be    "%s"' % (key, '"    or    "'.join(_method_params))))
            _method_params[key].update(method_params[key])
    def _unpack_epochs(epochs):
        if (len(epochs.event_id) > 1):
            epochs = [epochs[k] for k in epochs.event_id]
        else:
            epochs = [epochs]
        return epochs
    if (not isinstance(epochs, list)):
        epochs = _unpack_epochs(epochs)
    else:
        epochs = sum([_unpack_epochs(epoch) for epoch in epochs], [])
    if any((((epochs_t.baseline is None) and (epochs_t.info['highpass'] < 0.5) and keep_sample_mean) for epochs_t in epochs)):
        warn('Epochs    are    not    baseline    corrected,    covariance    matrix    may    be    inaccurate')
    orig = epochs[0].info['dev_head_t']
    if ((not isinstance(on_mismatch, string_types)) or (on_mismatch not in ['raise', 'warn', 'ignore'])):
        raise ValueError(('on_mismatch    must    be    "raise",    "warn",    or    "ignore",    got    %s' % on_mismatch))
    for (ei, epoch) in enumerate(epochs):
       ...