def METHOD_NAME(self, METHOD_NAME=True, channel=None, **kwargs):
    """Evaluate the function with the given values.
    Parameters
    ----------
    eval : bool; optional
        If true evaluate the model, otherwise simply compile the model.
        Defaults to True.
    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
    -------
    ndarray
        The value of the model at the times self.time.
    """
    if channel is None:
        nchan = self.nchan
        channels = np.arange(nchan)
    else:
        nchan = 1
        channels = [channel, ]
    hst_coeffs = np.zeros((nchan, 6)).tolist()
    if METHOD_NAME:
        lib = np
        model = self.fit
    else:
        lib = tt
        model = self.model
    # Parse 'h#' keyword arguments as coefficients
    for c in range(nchan):
        if self.nchannel_fitted > 1:
            chan = channels[c]
        else:
            chan = 0
        for i in range(6):
            try:
                if chan == 0:
                    hst_coeffs[c][i] = getattr(model, f'h{i}')
                else:
                    hst_coeffs[c][i] = getattr(model,
                                               f'h{i}_{chan}')
            except AttributeError:
                pass
    hst_flux = lib.zeros(0)
    for c in range(nchan):
        if self.nchannel_fitted > 1:
            chan = channels[c]
        else:
            chan = 0
        time = self.time_local
        if self.multwhite:
            # Split the arrays that have lengths of the original time axis
            time = split([time, ], self.nints, chan)[0]
        h0, h1, h2, h3, h4, h5 = hst_coeffs[c]
        # Batch time is relative to the start of each HST orbit
        # h5 is the orbital period of HST (~96 minutes)
        self.time_batch = self.time_local % h5
        lcpiece = (1+h0*lib.exp(-h1*self.time_batch + h2)
                   + h3*self.time_batch + h4*self.time_batch**2)
        hst_flux = lib.concatenate([hst_flux, lcpiece])
    return hst_flux