code: def _feature_window_function(
      window_type: str,
      window_size: int,
      blackman_coeff: float,
      device: torch.device,
      dtype: int,
  ) -> Tensor:
      r"""Returns a window function with the given type and size"""
      if window_type == HANNING:
          return torch.hann_window(
              window_size, periodic=False, device=device, dtype=dtype
          )
      elif window_type == HAMMING:
          return torch.hamming_window(
              window_size,
              periodic=False,
              alpha=0.54,
              beta=0.46,
              device=device,
              dtype=dtype,
          )
      elif window_type == POVEY:
          # like hanning but goes to zero at edges
          return torch.hann_window(
              window_size, periodic=False, device=device, dtype=dtype
          ).pow(0.85)
      elif window_type == RECTANGULAR:
          return torch.ones(window_size, device=device, dtype=dtype)
      elif window_type == BLACKMAN:
          a = 2 * math.pi / (window_size - 1)
          window_function = torch.arange(
              window_size, device=device, dtype=dtype
          )
          # can't use torch.blackman_window as they use different coefficients
          return (
              blackman_coeff
              - 0.5 * torch.cos(a * window_function)
              + (0.5 - blackman_coeff)
              * torch.cos(2 * a * window_function)
          ).to(device=device, dtype=dtype)
      else:
          raise Exception("Invalid window type " + window_type)
  
imports: import math
  from typing import Tuple
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: _feature_window_function(window_type, window_size, blackman_coeff, device, dtype) and returns: torch, torch, torch, torch, unknown