code: def get_framed_lengths(
      input_length, frame_size, hop_size, padding="center"
  ):
      """Give a strided framing, such as tf.signal.frame, gives output lengths.
  
      Args:
        input_length: Original length along the dimension to be framed.
        frame_size: Size of frames for striding.
        hop_size: Striding, space between frames.
        padding: Type of padding to apply, ['valid', 'same', 'center']. 'valid' is
          a no-op. 'same' applies padding to the end such that
          n_frames = n_t / hop_size. 'center' applies padding to both ends such that
          each frame timestamp is centered and n_frames = n_t / hop_size + 1.
  
      Returns:
        n_frames: Number of frames left after striding.
        padded_length: Length of the padded signal before striding.
      """
  
      # Use numpy since this function isn't used dynamically.
      def get_n_frames(length):
          return int(np.floor((length - frame_size) / hop_size)) + 1
  
      if padding == "valid":
          padded_length = input_length
          n_frames = get_n_frames(input_length)
  
      elif padding == "center":
          padded_length = input_length + frame_size
          n_frames = get_n_frames(padded_length)
  
      elif padding == "same":
          n_frames = int(np.ceil(input_length / hop_size))
          padded_length = (n_frames - 1) * hop_size + frame_size
  
      return n_frames, padded_length
  
imports: import crepe
  from ddsp import core
  from ddsp.core import safe_log
  from ddsp.core import tf_float32
  import gin
  import librosa
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
label_desc: Use image node_id 8 for calling a global function with example usage: get_framed_lengths(input_length, frame_size, hop_size, padding) and returns: n_frames,  padded_length, unknown