code: def pad_for_frame(vec, mode, frame_width, axis=0):
      """A helper function to pad vectors for input to tf.signal.frame.
  
      Each element in vec is the center of a frame if frame_step == 1 after padding.
  
      Args:
        vec: The vector to be padded along the first dimension.
        mode: Either 'front', 'center', or 'end'.
        frame_width: Width of frame to pad for.
        axis: Axis to pad.
  
      Returns:
        The padded vector of shape [vec.shape[0] + pad_size].
  
      Raises:
        ValueError: If 'mode' passed in is not 'front', 'center', or 'end'.
      """
      if mode == "front":
          pad_width_arg = (frame_width - 1, 0)
      elif mode == "center":
          # handles even and odd frame widths
          pad_width_arg = (
              int(frame_width / 2),
              frame_width - int(frame_width / 2) - 1,
          )
      elif mode == "end":
          pad_width_arg = (0, frame_width - 1)
      else:
          raise ValueError(f"Unrecognized pad mode {mode}.")
      return np.pad(
          vec,
          pad_width_arg,
          mode="constant",
          constant_values=(
              int(np.take(vec, 0, axis)),
              int(np.take(vec, -1, axis)),
          ),
      )
  
file_path: ddsp/ddsp/training/heuristics.py
imports: import ddsp
  import gin
  import note_seq
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 10 for calling a global function with example usage: pad_for_frame(vec, mode, frame_width, axis) and returns: np