code: def resample_frac(
      x: torch.Tensor,
      old_sr: int,
      new_sr: int,
      zeros: int = 24,
      rolloff: float = 0.945,
      output_length: Optional[int] = None,
      full: bool = False,
  ):
      """
      Functional version of `ResampleFrac`, refer to its documentation for more information.
  
      ..warning::
          If you call repeatidly this functions with the same sample rates, then the
          resampling kernel will be recomputed everytime. For best performance, you should use
          and cache an instance of `ResampleFrac`.
      """
      return ResampleFrac(old_sr, new_sr, zeros, rolloff).to(x)(
          x, output_length, full
      )
  
file_path: julius/julius/resample.py
imports: import math
  from typing import Optional
  import torch
  from torch.nn import functional
  from .core import sinc
  from .utils import simple_repr
label_desc: Use image node_id 1 for calling a global function with example usage: resample_frac(x, old_sr, new_sr, zeros, rolloff, output_length, full) and returns: ResampleFrac