code: def _downsample2(x, zeros=24):
      """
      Downsample x by a factor of two. The output length is half of the input, ceiled.
      Args:
          x (Tensor): signal to downsample, time should be the last dimension
          zeros (int): number of zero crossing to keep in the sinc filter.
  
      This function is kept only for reference, you should use the more generic `resample_frac`
      one. This function does not perform anti-aliasing filtering.
      """
      if x.shape[-1] % 2 != 0:
          x = F.pad(x, (0, 1))
      xeven = x[..., ::2]
      xodd = x[..., 1::2]
      *other, time = xodd.shape
      kernel = _kernel_upsample2_downsample2(zeros).to(x)
      out = xeven + F.conv1d(
          xodd.view(-1, 1, time), kernel, padding=zeros
      )[..., :-1].view(*other, time)
      return out.view(*other, -1).mul(0.5)
  
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 4 for calling a global function with example usage: _downsample2(x, zeros) and returns: out