code: def _farthest_point_sampler(
      data, batch_size, sample_points, dist, start_idx, result
  ):
      r"""Farthest Point Sampler
  
      Parameters
      ----------
      data : tensor
          A tensor of shape (N, d) where N is the number of points and d is the dimension.
      batch_size : int
          The number of batches in the ``data``. N should be divisible by batch_size.
      sample_points : int
          The number of points to sample in each batch.
      dist : tensor
          Pre-allocated tensor of shape (N, ) for to-sample distance.
      start_idx : tensor of int
          Pre-allocated tensor of shape (batch_size, ) for the starting sample in each batch.
      result : tensor of int
          Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index.
  
      Returns
      -------
      No return value. The input variable ``result`` will be overwriten with sampled indices.
  
      """
      assert F.shape(data)[0] >= sample_points * batch_size
      assert F.shape(data)[0] % batch_size == 0
  
      _CAPI_FarthestPointSampler(
          F.zerocopy_to_dgl_ndarray(data),
          batch_size,
          sample_points,
          F.zerocopy_to_dgl_ndarray(dist),
          F.zerocopy_to_dgl_ndarray(start_idx),
          F.zerocopy_to_dgl_ndarray(result),
      )
  
imports: import numpy
  from ..None import backend, ndarray
  from .._ffi.base import DGLError
  from .._ffi.function import _init_api
label_desc: Use image node_id 1 for calling a global function with example usage: _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result) without return types