human
Complete the below
def linear_regression_raw(raw, events, event_id=None, tmin=(-0.1), tmax=1, covariates=None, reject=None, flat=None, tstep=1.0, decim=1, picks=None, solver='cholesky'):

    ''''Estimate regression-based evoked potentials/fields by linear modeling.
    This models the full M/EEG time course, including correction for
    overlapping potentials and allowing for continuous/scalar predictors.
    Internally, this constructs a predictor matrix X of size
    n_samples * (n_conds * window length), solving the linear system
    ``Y = bX`` and returning ``b`` as evoked-like time series split by
    condition. See [1]_.
    Parameters
    raw : instance of Raw
    A raw object. Note: be very careful about data that is not
    downsampled, as the resulting matrices can be enormous and easily
    overload your computer. Typically, 100 Hz sampling rate is
    appropriate - or using the decim keyword (see below).
    events : ndarray of int, shape (n_events, 3)
    An array where the first column corresponds to samples in raw
    and the last to integer codes in event_id.
    event_id : dict | None
    As in Epochs.
a dictionary where the values may be integers or
    iterables of integers, corresponding to the 3rd column of
    events, and the keys are condition names.
    If None, uses all events in the events array.
    tmin : float | dict
    If float, gives the lower limit (in seconds) for the time window for
    which all event types\' effects are estimated. If a dict, can be used to
    specify time windows for specific event types: keys correspond to keys
    in event_id and/or covariates.
for missing values, the default (-.1) is
    used.
    tmax : float | dict
    If float, gives the upper limit (in seconds) for the time window for
    which all event types\' effects are estimated. If a dict, can be used to
    specify time windows for specific event types: keys correspond to keys
    in event_id and/or covariates.
for missing values, the default (1.) is
    used.
    covariates : dict-like | None
    If dict-like (e.g., a pandas DataFrame), values have to be array-like
    and of the same length as the rows in ```events```. Keys correspond
    to additional event types/conditions to...'''
assistant
#Here is the Python program you requested.
    if isinstance(solver, string_types):
        if (solver not in {'cholesky'}):
            raise ValueError('No    such    solver:    {0}'.format(solver))
        if (solver == 'cholesky'):
            def solver(X, y):
                a = (X.T * X).toarray()
                return linalg.solve(a, (X.T * y), sym_pos=True, overwrite_a=True, overwrite_b=True).T
    elif callable(solver):
        warn('When    using    a    custom    solver,    note    that    since    MNE    0.15,    this    function    will    pass    the    transposed    data    (n_channels,    n_times)    to    the    solver.    If    you    are    using    a    solver    that    expects    a    different    format,    it    will    give    wrong    results    and    might    in    extreme    cases    crash    your    session.')
    else:
        raise TypeError('The    solver    must    be    a    str    or    a    callable.')
    (data, info, events) = _prepare_rerp_data(raw, events, picks=picks, decim=decim)
    if (event_id is None):
        event_id = dict(((str(v), v) for v in set(events[:, 2])))
    (X, conds, cond_length, tmin_s, tmax_s) = _prepare_rerp_preds(n_samples=data.shape[1], sfreq=info['sfreq'], events=events, event_id=event_id, tmin=tmin, tmax=tmax, covariates=covariates)
    (X, data) = _clean_rerp_input(X, data, reject, flat, decim, info, tstep)
    coefs = solver(X, data.T)
    if (coefs.shape[0] != data.shape[0]):
        raise ValueError('solver    output    has    unexcepted    shape.    Supply    a    function    that    returns    coefficients    in    the    form    (n_targets,    n_features),    where    targets    ==    channels.')
    evokeds = _make_evokeds(coefs, conds, cond_length, tmin_s, tmax_s, info)
    return evokeds