code: def circular_correlation(
      a: torch.FloatTensor,
      b: torch.FloatTensor,
  ) -> torch.FloatTensor:
      """
      Compute the circular correlation between to vectors.
  
      .. note ::
          The implementation uses FFT.
  
      :param a: shape: s_1
          The tensor with the first vectors.
      :param b:
          The tensor with the second vectors.
  
      :return:
          The circular correlation between the vectors.
      """
      # Circular correlation of entity embeddings
      a_fft = torch.fft.rfft(a, dim=-1)
      b_fft = torch.fft.rfft(b, dim=-1)
      # complex conjugate
      a_fft = torch.conj(a_fft)
      # Hadamard product in frequency domain
      p_fft = a_fft * b_fft
      # inverse real FFT
      return torch.fft.irfft(p_fft, n=a.shape[-1], dim=-1)
  
imports: from __future__ import annotations
  import functools
  from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import broadcast_tensors, nn
  from .compute_kernel import batched_dot
  from .sim import KG2E_SIMILARITIES
  from ..typing import GaussianDistribution
  from ..utils import clamp_norm, einsum, is_cudnn_error, make_ones_like, negative_norm, negative_norm_of_sum, project_entity, tensor_product, tensor_sum
label_desc: Use image node_id 10 for calling a global function with example usage: circular_correlation(a, b) and returns: torch