code: def periodic_features(
      duration, max_period=None, min_period=None, **options
  ):
      r"""
      Create periodic (sin,cos) features from ``max_period`` down to
      ``min_period``.
  
      This is useful in time series models where long uneven seasonality can be
      treated via regression. When only ``max_period`` is specified this
      generates periodic features at all length scales. When also ``min_period``
      is specified this generates periodic features at large length scales, but
      omits high frequency features. This is useful when combining regression for
      long seasonality with other techniques like :func:`periodic_repeat` and
      :func:`periodic_cumsum` for short time scales. For example, to combine
      regress yearly seasonality down to the scale of one week one could set
      ``max_period=365.25`` and ``min_period=7``.
  
      :param int duration: Number of discrete time steps.
      :param float max_period: Optional max period, defaults to ``duration``.
      :param float min_period: Optional min period (exclusive), defaults to
          2 = Nyquist cutoff.
      :param \*\*options: Tensor construction options, e.g. ``dtype`` and
          ``device``.
      :returns: A ``(duration, 2 * ceil(max_period / min_period) - 2)``-shaped
          tensor of features normalized to lie in [-1,1].
      :rtype: ~torch.Tensor
      """
      assert isinstance(duration, int) and duration >= 0
      if max_period is None:
          max_period = duration
      if min_period is None:
          min_period = 2
      assert 2 <= min_period, "min_period is below Nyquist cutoff"
      assert min_period <= max_period
  
      t = (
          torch.arange(float(duration), **options)
          .unsqueeze(-1)
          .unsqueeze(-1)
      )
      phase = torch.tensor([0, math.pi / 2], **options).unsqueeze(-1)
      freq = torch.arange(1, max_period / min_period, **options).mul_(
          2 * math.pi / max_period
      )
      result = (
          (freq * t + phase).cos_().reshape(duration, -1).contiguous()
      )
      return result
  
imports: import math
  import torch
  from torch.fft import irfft, rfft
  from ..None import settings
label_desc: Use image node_id 7 for calling a global function with example usage: periodic_features(duration, max_period, min_period) and returns: result