code: def random_scale_point_cloud(
      batch_data, scale_low=0.8, scale_high=1.25
  ):
      """Randomly scale the point cloud. Scale is per point cloud.
      Input:
          BxNx3 array, original batch of point clouds
      Return:
          BxNx3 array, scaled batch of point clouds
      """
      B, N, C = batch_data.shape
      scales = np.random.uniform(scale_low, scale_high, B)
      for batch_index in range(B):
          batch_data[batch_index, :, :] *= scales[batch_index]
      return batch_data
  
imports: from lavis.common.registry import registry
  from lavis.processors.blip_processors import BlipImageBaseProcessor
  from omegaconf import OmegaConf
  import torchvision.transforms
  from lavis.models.ulip_models.utils.io import IO
  import numpy
  from PIL import Image
  import torch
label_desc: Use image node_id 8 for calling a global function with example usage: random_scale_point_cloud(batch_data, scale_low, scale_high) and returns: batch_data