code: def pad(
      x,
      frame_size,
      hop_size,
      padding="center",
      axis=1,
      mode="CONSTANT",
      constant_values=0,
  ):
      """Pad a tensor for strided framing such as tf.signal.frame.
  
      Args:
        x: Tensor to pad, any shape.
        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.
        axis: Axis along which to pad `x`.
        mode: Padding mode for tf.pad(). One of "CONSTANT", "REFLECT", or
          "SYMMETRIC" (case-insensitive).
        constant_values: Passthrough kwarg for tf.pad().
  
      Returns:
        A padded version of `x` along axis. Output sizes can be computed separately
          with strided_lengths.
      """
      x = tf_float32(x)
  
      if padding == "valid":
          return x
  
      if hop_size > frame_size:
          raise ValueError(
              f"During padding, frame_size ({frame_size})"
              f" must be greater than hop_size ({hop_size})."
          )
  
      if len(x.shape) <= 1:
          axis = 0
  
      n_t = x.shape[axis]
      _, n_t_padded = get_framed_lengths(
          n_t, frame_size, hop_size, padding
      )
      pads = [[0, 0] for _ in range(len(x.shape))]
  
      if padding == "same":
          pad_amount = int(n_t_padded - n_t)
          pads[axis] = [0, pad_amount]
  
      elif padding == "center":
          pad_amount = int(
              frame_size // 2
          )  # Symmetric even padding like librosa.
          pads[axis] = [pad_amount, pad_amount]
  
      else:
          raise ValueError(
              "`padding` must be one of ['center', 'same'"
              f"'valid'], received ({padding})."
          )
  
      return tf.pad(x, pads, mode=mode, constant_values=constant_values)
  
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 9 for calling a global function with example usage: pad(x, frame_size, hop_size, padding, axis, mode, constant_values) and returns: tf, x