<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def dac(self, expanded=False, return_res=64, inplace=False):
 """ Performs the digital to analogue conversion of the signal stored in `d_signal` if expanded is False, or `e_d_signal` if expanded is True. The d_signal/e_d_signal, fmt, gain, and baseline fields must all be valid. If inplace is True, the dac will be performed inplace on the variable, the p_signal/e_p_signal attribute will be set, and the d_signal/e_d_signal field will be set to None. Parameters expanded : bool, optional Whether to transform the `e_d_signal attribute` (True) or the `d_signal` attribute (False). inplace : bool, optional Whether to automatically set the object's corresponding physical signal attribute and set the digital signal attribute to None (True), or to return the converted signal as a separate variable without changing the original digital signal attribute (False). Returns ------- p_signal : numpy array, optional The physical conversion of the signal. Either a 2d numpy array or a list of 1d numpy arrays. Examples -------- """


        # The digital nan values for each channel
        d_nans = _digi_nan(self.fmt)

        # Get the appropriate float dtype
        if return_res == 64:
            floatdtype = 'float64'
        elif return_res == 32:
            floatdtype = 'float32'
        else:
            floatdtype = 'float16'

        # Do inplace conversion and set relevant variables.
        if inplace:
            if expanded:
                for ch in range(self.n_sig):
                    # nan locations for the channel
                    ch_nanlocs = self.e_d_signal[ch] == d_nans[ch]
                    self.e_d_signal[ch] = self.e_d_signal[ch].astype(floatdtype, copy=False)
                    np.subtract(self.e_d_signal[ch], self.baseline[ch], self.e_d_signal[ch])
                    np.divide(self.e_d_signal[ch], self.adc_gain[ch], self.e_d_signal[ch])
                    self.e_d_signal[ch][ch_nanlocs] = np.nan
                self.e_p_signal = self.e_d_signal
                self.e_d_signal = None
            else:
                nanlocs = self.d_signal == d_nans
                # Do float conversion immediately to avoid potential under/overflow
                # of efficient int dtype
                self.d_signal = self.d_signal.astype(floatdtype, copy=False)
                np.subtract(self.d_signal, self.baseline, self.d_signal)
                np.divide(self.d_signal, self.adc_gain, self.d_signal)
                self.d_signal[nanlocs] = np.nan
                self.p_signal = self.d_signal
                self.d_signal = None

        # Return the variable
        else:
            if expanded:
                p_signal = []
                for ch in range(self.n_sig):
                    # nan locations for the channel
                    ch_nanlocs = self.e_d_signal[ch] == d_nans[ch]
                    ch_p_signal = self.e_d_signal[ch].astype(floatdtype, copy=False)
                    np.subtract(ch_p_signal, self.baseline[ch], ch_p_signal)
                    np.divide(ch_p_signal, self.adc_gain[ch], ch_p_signal)
                    ch_p_signal[ch_nanlocs] = np.nan
                    p_signal.append(ch_p_signal)
            else:
                nanlocs = self.d_signal == d_nans
                p_signal = self.d_signal.astype(floatdtype, copy=False)
                np.subtract(p_signal, self.baseline, p_signal)
                np.divide(p_signal, self.adc_gain, p_signal)
                p_signal[nanlocs] = np.nan

            return p_signal