code: def get_shapes(scannet=False):
      """Get the shapes."""
      cluster_filepath = os.path.join(
          FLAGS.tfrecords_dir,
          "dict_clusterCenter_class_nearestModel.pkl",
      )
      with gfile.Open(cluster_filepath, "rb") as file:
          dict_clusters = pickle.load(file)
  
      shape_centers = []
      shape_sdfs = []
      shape_pointclouds = []
      for _, cluster in sorted(dict_clusters.items()):
          center, class_id, model_name = cluster
          path_prefix = os.path.join(
              FLAGS.shapenet_dir, class_id, model_name
          )
          file_sdf = os.path.join(
              path_prefix, "model_normalized_sdf.npy"
          )
          file_pointcloud = os.path.join(
              path_prefix, "model_normalized_points.npy"
          )
          with gfile.Open(file_sdf, "rb") as f:
              sdf = np.load(f).astype(np.float32)
          with gfile.Open(file_pointcloud, "rb") as f:
              pointcloud = np.load(f).astype(np.float32)
          if scannet:
              rot = np.reshape(
                  np.array(
                      [0, 0, 1, 0, 1, 0, -1, 0, 0], dtype=np.float32
                  ),
                  [3, 3],
              )
              # rot = np.reshape(np.array([0, 1, 0, -1, 0, 0, 0, 0, 1],
              #                           dtype=np.float32), [3, 3])
              # rot = np.reshape(np.array([0, -1, 0, -1, 0, 0, 0, 0, 1],
              #                           dtype=np.float32), [3, 3])
              pointcloud = np.transpose(rot @ np.transpose(pointcloud))
          shape_centers.append(np.reshape(center, [1, 32, 32, 32]))
          shape_sdfs.append(np.reshape(sdf, [1, 32, 32, 32]))
          shape_pointclouds.append(np.reshape(pointcloud, [1, -1, 3]))
  
      shape_centers = np.concatenate(shape_centers, axis=0)
      shape_sdfs = np.concatenate(shape_sdfs, axis=0)
      shape_pointclouds = np.concatenate(shape_pointclouds, axis=0)
      return shape_centers, shape_sdfs, shape_pointclouds, dict_clusters
  
imports: import os
  import pickle
  import time
  from absl import app
  from absl import flags
  from absl import logging
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorflow_graphics.projects.points_to_3Dobjects.data_preparation.extract_protos
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import collision_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import cross_entropy_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import focal_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import focal_loss_sparse
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import learning_rate_schedule
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import regression_huber_loss
  from tensorflow_graphics.projects.points_to_3Dobjects.losses import regression_l1_loss
  import tensorflow_graphics.projects.points_to_3Dobjects.models.centernet_vid
  from tensorflow_graphics.projects.points_to_3Dobjects.transforms import transforms_factory
  from tensorflow_graphics.projects.points_to_3Dobjects.utils import evaluator
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.io
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.logger
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.plot
  import tensorflow_graphics.projects.points_to_3Dobjects.utils.tf_utils
  from google3.pyglib import gfile
label_desc: Use image node_id 1 for calling a global function with example usage: get_shapes(scannet) and returns: shape_centers,  shape_sdfs,  shape_pointclouds,  dict_clusters