code: def apply_beamforming_vector(
      beamform_vector: torch.Tensor, mix: torch.Tensor
  ) -> torch.Tensor:
      # (..., C) x (..., C, T) -> (..., T)
      es = torch.einsum("...c,...ct->...t", beamform_vector.conj(), mix)
      return es
  
imports: from typing import List, Union
  import torch
  import torch_complex.functional
  import torchaudio
label_desc: Use image node_id 5 for calling a global function with example usage: apply_beamforming_vector(beamform_vector, mix) and returns: es