code: def periodic_repeat(tensor, size, dim):
      """
      Repeat a ``period``-sized tensor up to given ``size``. For example::
  
          >>> x = torch.tensor([[1, 2, 3], [4, 5, 6]])
          >>> periodic_repeat(x, 4, 0)
          tensor([[1, 2, 3],
                  [4, 5, 6],
                  [1, 2, 3],
                  [4, 5, 6]])
          >>> periodic_repeat(x, 4, 1)
          tensor([[1, 2, 3, 1],
                  [4, 5, 6, 4]])
  
      This is useful for computing static seasonality in time series models.
  
      :param torch.Tensor tensor: A tensor of differences.
      :param int size: Desired size of the result along dimension ``dim``.
      :param int dim: The tensor dimension along which to repeat.
      """
      assert isinstance(size, int) and size >= 0
      assert isinstance(dim, int)
      if dim >= 0:
          dim -= tensor.dim()
  
      period = tensor.size(dim)
      repeats = [1] * tensor.dim()
      repeats[dim] = (size + period - 1) // period
      result = tensor.repeat(*repeats)
      result = result[
          (Ellipsis, slice(None, size)) + (slice(None),) * (-1 - dim)
      ]
      return result
  
imports: import math
  import torch
  from torch.fft import irfft, rfft
  from ..None import settings
label_desc: Use image node_id 5 for calling a global function with example usage: periodic_repeat(tensor, size, dim) and returns: result