code: def _get_window(
      waveform: Tensor,
      padded_window_size: int,
      window_size: int,
      window_shift: int,
      window_type: str,
      blackman_coeff: float,
      snip_edges: bool,
      raw_energy: bool,
      energy_floor: float,
      dither: float,
      remove_dc_offset: bool,
      preemphasis_coefficient: float,
  ) -> Tuple[Tensor, Tensor]:
      r"""Gets a window and its log energy
  
      Returns:
          (Tensor, Tensor): strided_input of size (m, ``padded_window_size``) and signal_log_energy of size (m)
      """
      device, dtype = waveform.device, waveform.dtype
      epsilon = _get_epsilon(device, dtype)
  
      # size (m, window_size)
      strided_input = _get_strided(
          waveform, window_size, window_shift, snip_edges
      )
  
      if dither != 0.0:
          rand_gauss = torch.randn(
              strided_input.shape, device=device, dtype=dtype
          )
          strided_input = strided_input + rand_gauss * dither
  
      if remove_dc_offset:
          # Subtract each row/frame by its mean
          row_means = torch.mean(strided_input, dim=1).unsqueeze(
              1
          )  # size (m, 1)
          strided_input = strided_input - row_means
  
      if raw_energy:
          # Compute the log energy of each row/frame before applying preemphasis and
          # window function
          signal_log_energy = _get_log_energy(
              strided_input, epsilon, energy_floor
          )  # size (m)
  
      if preemphasis_coefficient != 0.0:
          # strided_input[i,j] -= preemphasis_coefficient * strided_input[i, max(0, j-1)] for all i,j
          offset_strided_input = torch.nn.functional.pad(
              strided_input.unsqueeze(0), (1, 0), mode="replicate"
          ).squeeze(
              0
          )  # size (m, window_size + 1)
          strided_input = (
              strided_input
              - preemphasis_coefficient * offset_strided_input[:, :-1]
          )
  
      # Apply window_function to each row/frame
      window_function = _feature_window_function(
          window_type, window_size, blackman_coeff, device, dtype
      ).unsqueeze(
          0
      )  # size (1, window_size)
      strided_input = (
          strided_input * window_function
      )  # size (m, window_size)
  
      # Pad columns with zero until we reach size (m, padded_window_size)
      if padded_window_size != window_size:
          padding_right = padded_window_size - window_size
          strided_input = torch.nn.functional.pad(
              strided_input.unsqueeze(0),
              (0, padding_right),
              mode="constant",
              value=0,
          ).squeeze(0)
  
      # Compute energy after window function (not the raw one)
      if not raw_energy:
          signal_log_energy = _get_log_energy(
              strided_input, epsilon, energy_floor
          )  # size (m)
  
      return strided_input, signal_log_energy
  
imports: import math
  from typing import Tuple
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 7 for calling a global function with example usage: _get_window(waveform, padded_window_size, window_size, window_shift, window_type, blackman_coeff, snip_edges, raw_energy, energy_floor, dither, remove_dc_offset, preemphasis_coefficient) and returns: strided_input,  signal_log_energy