code: def wpe_one_iteration(
      Y: Union[torch.Tensor, ComplexTensor],
      power: torch.Tensor,
      taps: int = 10,
      delay: int = 3,
      eps: float = 1e-10,
      inverse_power: bool = True,
  ) -> Union[torch.Tensor, ComplexTensor]:
      """WPE for one iteration
  
      Args:
          Y: Complex valued STFT signal with shape (..., C, T)
          power: : (..., T)
          taps: Number of filter taps
          delay: Delay as a guard interval, such that X does not become zero.
          eps:
          inverse_power (bool):
      Returns:
          enhanced: (..., C, T)
      """
      assert Y.size()[:-2] == power.size()[:-1]
      batch_freq_size = Y.size()[:-2]
      Y = Y.view(-1, *Y.size()[-2:])
      power = power.view(-1, power.size()[-1])
  
      if inverse_power:
          inverse_power = 1 / torch.clamp(power, min=eps)
      else:
          inverse_power = power
  
      correlation_matrix, correlation_vector = get_correlations(
          Y, inverse_power, taps, delay
      )
      filter_matrix_conj = get_filter_matrix_conj(
          correlation_matrix, correlation_vector
      )
      enhanced = perform_filter_operation(
          Y, filter_matrix_conj, taps, delay
      )
  
      enhanced = enhanced.view(*batch_freq_size, *Y.size()[-2:])
      return enhanced
  
file_path: espnet/espnet2/enh/layers/wpe.py
imports: from typing import Tuple, Union
  import torch
  import torch.nn.functional
  import torch_complex.functional
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import einsum, matmul, reverse
label_desc: Use image node_id 6 for calling a global function with example usage: wpe_one_iteration(Y, power, taps, delay, eps, inverse_power) and returns: enhanced