code: def resample(x, factor: int):
      batch, frame, channel = x.shape
      x = x.permute(0, 2, 1).reshape(batch * channel, 1, frame)
  
      window = torch.hann_window(
          factor * 2,
          dtype=x.dtype,
          device=x.device,
      ).reshape(1, 1, -1)
      y = torch.zeros(x.shape[0], x.shape[1], factor * x.shape[2]).to(x)
      y[..., ::factor] = x
      y[..., -1:] = x[..., -1:]
      y = torch.nn.functional.pad(y, [factor, factor])
      y = torch.nn.functional.conv1d(y, window)[..., :-1]
  
      y = y.reshape(batch, channel, factor * frame).permute(0, 2, 1)
  
      return y
  
imports: import math
  import librosa
  import numpy
  import torch
  import torch.fft
  import torch.nn
  from scipy.signal import get_window
label_desc: Use image node_id 4 for calling a global function with example usage: resample(x, factor) and returns: y