def METHOD_NAME(self, channel=None, **kwargs):
    """Evaluate the function with the given values.
    Parameters
    ----------
    channel : int; optional
        If not None, only consider one of the channels. Defaults to None.
    **kwargs : dict
        Must pass in the time array here if not already set.
    Returns
    -------
    lcfinal : ndarray
        The value of the model at the times self.time.
    """
    if channel is None:
        nchan = self.nchannel_fitted
        channels = self.fitted_channels
    else:
        nchan = 1
        channels = [channel, ]
    # Get the time
    if self.time is None:
        self.time = kwargs.get('time')
    # Create the ramp from the coeffs
    lcfinal = np.ones((nchan, len(self.time)))
    for c in range(nchan):
        if self.nchannel_fitted > 1:
            chan = channels[c]
        else:
            chan = 0
        time = self.time
        if self.multwhite:
            # Split the arrays that have lengths of the original time axis
            time = split([time, ], self.nints, chan)[0]
        for s in np.where(self.steps[c] != 0)[0]:
            lcfinal[c, time >= self.steptimes[c, s]] += \
                self.steps[c, s]
    return lcfinal.flatten()