code: def seprate_point_cloud(
      xyz, num_points, crop, fixed_points=None, padding_zeros=False
  ):
      """
      seprate point cloud: usage : using to generate the incomplete point cloud with a setted number.
      """
      _, n, c = xyz.shape
  
      assert n == num_points
      assert c == 3
      if crop == num_points:
          return xyz, None
  
      INPUT = []
      CROP = []
      for points in xyz:
          if isinstance(crop, list):
              num_crop = random.randint(crop[0], crop[1])
          else:
              num_crop = crop
  
          points = points.unsqueeze(0)
  
          if fixed_points is None:
              center = F.normalize(
                  torch.randn(1, 1, 3), p=2, dim=-1
              ).cuda()
          else:
              if isinstance(fixed_points, list):
                  fixed_point = random.sample(fixed_points, 1)[0]
              else:
                  fixed_point = fixed_points
              center = fixed_point.reshape(1, 1, 3).cuda()
  
          distance_matrix = torch.norm(
              center.unsqueeze(2) - points.unsqueeze(1), p=2, dim=-1
          )  # 1 1 2048
  
          idx = torch.argsort(
              distance_matrix, dim=-1, descending=False
          )[
              0, 0
          ]  # 2048
  
          if padding_zeros:
              input_data = points.clone()
              input_data[0, idx[:num_crop]] = (
                  input_data[0, idx[:num_crop]] * 0
              )
  
          else:
              input_data = points.clone()[0, idx[num_crop:]].unsqueeze(
                  0
              )  # 1 N 3
  
          crop_data = points.clone()[0, idx[:num_crop]].unsqueeze(0)
  
          if isinstance(crop, list):
              INPUT.append(fps(input_data, 2048))
              CROP.append(fps(crop_data, 2048))
          else:
              INPUT.append(input_data)
              CROP.append(crop_data)
  
      input_data = torch.cat(INPUT, dim=0)  # B N 3
      crop_data = torch.cat(CROP, dim=0)  # B M 3
  
      return input_data.contiguous(), crop_data.contiguous()
  
imports: import numpy
  import matplotlib.pyplot
  from mpl_toolkits.mplot3d import Axes3D
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  import os
  from collections import abc
label_desc: Use image node_id 9 for calling a global function with example usage: seprate_point_cloud(xyz, num_points, crop, fixed_points, padding_zeros) and returns: input_data,  crop_data, xyz,  None