code: def interpolate_f0(f0):
      try:
          from scipy.interpolate import interp1d
      except ImportError:
          raise "Please install scipy (`pip install scipy`)"
  
      orig_t = np.arange(f0.shape[0])
      f0_interp = f0[:]
      ii = f0_interp != 0
      if ii.sum() > 1:
          f0_interp = interp1d(
              orig_t[ii],
              f0_interp[ii],
              bounds_error=False,
              kind="linear",
              fill_value=0,
          )(orig_t)
          f0_interp = torch.Tensor(f0_interp).type_as(f0).to(f0.device)
      return f0_interp
  
imports: import json
  import logging
  import os
  import random
  from pathlib import Path
  import numpy
  import torch
  import torch.utils.data
  from .None import data_utils
  from fairseq.data.fairseq_dataset import FairseqDataset
label_desc: Use image node_id 2 for calling a global function with example usage: interpolate_f0(f0) and returns: f0_interp