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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def detect(self, x, conf, adc_zero):
 """ Run detection. x is digital signal """

        self.c = conf
        self.annotations = []
        self.sample_valid = False

        if len(x) < 1:
            return []

        self.x = x
        self.adc_zero = adc_zero

        self.qfv = np.zeros((self.c._BUFLN), dtype="int64")
        self.smv = np.zeros((self.c._BUFLN), dtype="int64")
        self.v1 = 0

        t0 = 0
        self.tf = len(x) - 1
        self.t = 0 - self.c.dt4

        self.annot = GQRS.Annotation(0, "NOTE", 0, 0)

        # Cicular buffer of Peaks
        first_peak = GQRS.Peak(0, 0, 0)
        tmp = first_peak
        for _ in range(1, self.c._NPEAKS):
            tmp.next_peak = GQRS.Peak(0, 0, 0)
            tmp.next_peak.prev_peak = tmp
            tmp = tmp.next_peak
        tmp.next_peak = first_peak
        first_peak.prev_peak = tmp
        self.current_peak = first_peak

        if self.c.spm > self.c._BUFLN:
            if self.tf - t0 > self.c._BUFLN:
                tf_learn = t0 + self.c._BUFLN - self.c.dt4
            else:
                tf_learn = self.tf - self.c.dt4
        else:
            if self.tf - t0 > self.c.spm:
                tf_learn = t0 + self.c.spm - self.c.dt4
            else:
                tf_learn = self.tf - self.c.dt4

        self.countdown = -1
        self.state = "LEARNING"
        self.gqrs(t0, tf_learn)

        self.rewind_gqrs()

        self.state = "RUNNING"
        self.t = t0 - self.c.dt4
        self.gqrs(t0, self.tf)

        return self.annotations