code: def farthest_point_sample(xyz, npoint):
      """
      Input:
          xyz: pointcloud data, [B, N, 2]
          npoint: number of samples
      Return:
          centroids: sampled pointcloud index, [B, npoint]
      """
      device = xyz.device
      B, N, C = xyz.shape
      centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
      distance = torch.ones(B, N).to(device) * 1e10
      farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
      batch_indices = torch.arange(B, dtype=torch.long).to(device)
      for i in range(npoint):
          centroids[:, i] = farthest
          centroid = xyz[batch_indices, farthest, :].view(B, 1, 2)
          dist = torch.sum((xyz - centroid) ** 2, -1)
          distance = torch.min(distance, dist)
          farthest = torch.max(distance, -1)[1]
      return centroids
  
imports: from abc import ABC, abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  import math
  from .multimodal_encoder.builder import build_vision_tower
  import pdb
  from ferret.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
label_desc: Use image node_id 4 for calling a global function with example usage: farthest_point_sample(xyz, npoint) and returns: centroids