code: def perform_WPD_filtering(
      filter_matrix: Union[torch.Tensor, ComplexTensor],
      Y: Union[torch.Tensor, ComplexTensor],
      bdelay: int,
      btaps: int,
  ) -> Union[torch.Tensor, ComplexTensor]:
      """Perform WPD filtering.
  
      Args:
          filter_matrix: Filter matrix (B, F, (btaps + 1) * C)
          Y : Complex STFT signal with shape (B, F, C, T)
  
      Returns:
          enhanced (torch.complex64/ComplexTensor): (B, F, T)
      """
      # (B, F, C, T) --> (B, F, C, T, btaps + 1)
      Ytilde = signal_framing(
          Y, btaps + 1, 1, bdelay, do_padding=True, pad_value=0
      )
      Ytilde = reverse(Ytilde, dim=-1)
  
      Bs, Fdim, C, T = Y.shape
      # --> (B, F, T, btaps + 1, C) --> (B, F, T, (btaps + 1) * C)
      Ytilde = (
          Ytilde.permute(0, 1, 3, 4, 2)
          .contiguous()
          .view(Bs, Fdim, T, -1)
      )
      # (B, F, T, 1)
      enhanced = einsum(
          "...tc,...c->...t", Ytilde, filter_matrix.conj()
      )
      return enhanced
  
imports: from typing import List, Union
  import torch
  from packaging.version import parse
  from torch_complex import functional
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import cat, complex_norm, einsum, inverse, is_complex, is_torch_complex_tensor, matmul, reverse, solve, to_double
label_desc: Use image node_id 21 for calling a global function with example usage: perform_WPD_filtering(filter_matrix, Y, bdelay, btaps) and returns: enhanced