code: def as_complex(x):
      """
      Similar to :func:`torch.view_as_complex` but copies data in case strides
      are not multiples of two.
      """
      if any(stride % 2 for stride in x.stride()[:-1]):
          # First try to normalize strides.
          x = x.squeeze().reshape(x.shape)
      if any(stride % 2 for stride in x.stride()[:-1]):
          # Fall back to copying data.
          x = x.clone()
      return torch.view_as_complex(x)
  
imports: import math
  import torch
  from torch.fft import irfft, rfft
  from ..None import settings
label_desc: Use image node_id 2 for calling a global function with example usage: as_complex(x) and returns: torch