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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 gate(self, tzero=1.0, tpad=0.5, whiten=True, threshold=50., cluster_window=0.5, **whiten_kwargs):
 """Removes high amplitude peaks from data using inverse Planck window. Points will be discovered automatically using a provided threshold and clustered within a provided time window. Parameters tzero : `int`, optional half-width time duration in which the time series is set to zero tpad : `int`, optional half-width time duration in which the Planck window is tapered whiten : `bool`, optional if True, data will be whitened before gating points are discovered, use of this option is highly recommended threshold : `float`, optional amplitude threshold, if the data exceeds this value a gating window will be placed cluster_window : `float`, optional time duration over which gating points will be clustered **whiten_kwargs other keyword arguments that will be passed to the `TimeSeries.whiten` method if it is being used when discovering gating points Returns ------- out : `~gwpy.timeseries.TimeSeries` a copy of the original `TimeSeries` that has had gating windows applied Examples -------- Read data into a `TimeSeries` Apply gating using custom arguments fftlength=4, overlap=2, method='median') Plot the original data and the gated data, whiten both for visualization purposes label='Ungated', color='dodgerblue', zorder=2) color='orange', zorder=3) """

        try:
            from scipy.signal import find_peaks
        except ImportError as exc:
            exc.args = ("Must have scipy>=1.1.0 to utilize this method.",)
            raise
        # Find points to gate based on a threshold
        data = self.whiten(**whiten_kwargs) if whiten else self
        window_samples = cluster_window * data.sample_rate.value
        gates = find_peaks(abs(data.value), height=threshold,
                           distance=window_samples)[0]
        out = self.copy()

        # Iterate over list of indices to gate and apply each one
        nzero = int(abs(tzero) * self.sample_rate.value)
        npad = int(abs(tpad) * self.sample_rate.value)
        half = nzero + npad
        ntotal = 2 * half
        for gate in gates:
            # Set the boundaries for windowed data in the original time series
            left_idx = max(0, gate - half)
            right_idx = min(gate + half, len(self.value) - 1)

            # Choose which part of the window will replace the data
            # This must be done explicitly for edge cases where a window
            # overlaps index 0 or the end of the time series
            left_idx_window = half - (gate - left_idx)
            right_idx_window = half + (right_idx - gate)

            window = 1 - planck(ntotal, nleft=npad, nright=npad)
            window = window[left_idx_window:right_idx_window]
            out[left_idx:right_idx] *= window

        return out