code: def get_padded_features(
      features: torch.Tensor,
      num_frames: int,
  ) -> torch.Tensor:
      """
      Splits the temporal dimension (of length T) of MFCC features into
      @num_frames sub-vectors (of length ``ceil(T/num_frames)``).
      As T may not be divisible by @num_frames, pads the temporal dimension if required.
  
      Args:
          features: Tensor[batchsize x C(num_audio_channels) x num_mfccs x T]
          num_frames: number of padded sub-vectors
  
      Returns:
          padded_features: Tensor (batchsize x C x num_mfccs x num_frames x ceil(T/num_frames))
      """
      N, C, num_mfccs, T = features.shape
      frame_length = math.ceil(T / num_frames)
  
      if T % num_frames != 0:
          padded_features = torch.zeros(
              [
                  N,
                  C,
                  num_mfccs,
                  frame_length * num_frames,
              ],
              dtype=features.dtype,
              device=features.device,
          )
          padded_features[:, :, :, :T] = features
      else:
          padded_features = features
  
      padded_features = padded_features.reshape(
          N, C, num_mfccs, num_frames, frame_length
      )
      return padded_features
  
imports: import math
  import torch
  from utils import logger
label_desc: Use image node_id 4 for calling a global function with example usage: get_padded_features(features, num_frames) and returns: padded_features