How to use the torchmeta.toy.sinusoid.SinusoidTask function in torchmeta

def __init__(self, amplitude, phase, input_range, noise_std, num_samples,
                 transform=None, target_transform=None):
        super(SinusoidTask, self).__init__(None) # Regression task
        self.amplitude = amplitude
        self.phase = phase
        self.input_range = input_range
        self.num_samples = num_samples
        self.noise_std = noise_std

        self.transform = transform
        self.target_transform = target_transform

        self._inputs = np.random.uniform(input_range[0], input_range[1],
            size=(num_samples, 1))
        self._targets = amplitude * np.sin(self._inputs - phase)
        if (noise_std is not None) and (noise_std > 0.):
            self._targets += noise_std * np.random.randn(num_samples, 1)

def __getitem__(self, index):
        if self._is_sinusoid[index]:
            amplitude, phase = self._amplitudes[index], self._phases[index]
            task = SinusoidTask(amplitude, phase, self._input_range, self.noise_std,
                self.num_samples_per_task, self.transform, self.target_transform)
        else:
            slope, intercept = self._slopes[index], self._intercepts[index]
            task = LinearTask(slope, intercept, self._input_range, self.noise_std,
                self.num_samples_per_task, self.transform, self.target_transform)

        if self.dataset_transform is not None:
            task = self.dataset_transform(task)

        return task

def __getitem__(self, index):
        amplitude, phase = self._amplitudes[index], self._phases[index]
        task = SinusoidTask(amplitude, phase, self._input_range, self.noise_std,
            self.num_samples_per_task, self.transform, self.target_transform)

        if self.dataset_transform is not None:
            task = self.dataset_transform(task)

        return task