code: def _get_sinc_resample_kernel(self, dtype=None):
      orig_freq, new_freq = (int(self.orig_freq) // self.gcd), (
          int(self.new_freq) // self.gcd
      )
      if self.lowpass_filter_width <= 0:
          raise ValueError("Low pass filter width should be positive.")
      base_freq = min(orig_freq, new_freq)
      base_freq *= self.rolloff
      width = math.ceil(
          self.lowpass_filter_width * orig_freq / base_freq
      )
      idx = (
          Tensor.arange(
              -width,
              width + orig_freq,
              dtype=(dtype if dtype is not None else dtypes.float32),
          )[None, None]
          / orig_freq
      )
      t = (
          Tensor.arange(0, -new_freq, -1, dtype=dtype)[:, None, None]
          / new_freq
          + idx
      )
      t *= base_freq
      t = t.clip(-self.lowpass_filter_width, self.lowpass_filter_width)
      window = (t * math.pi / self.lowpass_filter_width / 2).cos() ** 2
      t *= math.pi
      scale = base_freq / orig_freq
      kernels = Tensor.where(
          t == 0, Tensor(1.0, dtype=t.dtype).to(t.device), t.sin() / t
      )
      kernels *= window * scale
      if dtype is None:
          kernels = kernels.cast(dtype=dtypes.float32)
      return kernels, width
  
imports: import math
  from typing import Optional, Tuple
  from tinygrad import Tensor, dtypes
  import librosa
  import soundfile
  import numpy
  import parselmouth
label_desc: Use image node_id 4 for calling the Resample obj's underlying member method code with example usage: obj._get_sinc_resample_kernel(dtype) and returns: kernels,  width