class_docstr: Sliding Window.
  
  Provides a sliding window over a batched continuous raw audio tensor.
  Optionally, provides padding (Currently not implemented).
  Combine this module with a pre-encoder compatible with raw audio data,
  for example Sinc convolutions.
  
  Known issues:
  Output length is calculated incorrectly if audio shorter than win_length.
  WARNING: trailing values are discarded - padding not implemented yet.
  There is currently no additional window function applied to input values.
docstr: Initialize.
  
  Args:
      win_length: Length of frame.
      hop_length: Relative starting point of next frame.
      channels: Number of input channels.
      padding: Padding (placeholder, currently not implemented).
      fs:  Sampling rate (placeholder for compatibility, not used).
code: def __init__(
      self,
      win_length: int = 400,
      hop_length: int = 160,
      channels: int = 1,
      padding: int = None,
      fs=None,
  ):
      """Initialize.
  
      Args:
          win_length: Length of frame.
          hop_length: Relative starting point of next frame.
          channels: Number of input channels.
          padding: Padding (placeholder, currently not implemented).
          fs:  Sampling rate (placeholder for compatibility, not used).
      """
      assert check_argument_types()
      super().__init__()
      self.fs = fs
      self.win_length = win_length
      self.hop_length = hop_length
      self.channels = channels
      self.padding = padding
  
file_path: espnet/espnet2/asr/frontend/windowing.py
imports: from typing import Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
label_desc: Use image node_id 1 to create a new SlidingWindow object from inherited base classes: AbsFrontend with example: obj = SlidingWindow(win_length, hop_length, channels, padding, fs)