class_docstr: Transform for running a PointNet inference on a Data object. It assumes that the
  model has been trained for segmentation.
  
  Parameters
  ----------
  checkpoint_dir: str
      Path to a checkpoint directory
  model_name: str
      Model name, the file ``checkpoint_dir/model_name.pt`` must exist
  weight_name: str
      Type of weights to load (best for iou, best for loss etc...)
  feat_name: str
      Name of the key in Data that will hold the output of the forward
  num_classes: int
      Number of classes that the model was trained on
code: def __repr__(self):
      return "{}(model: {}, transform: {})".format(
          self.__class__.__name__,
          self.model.__class__.__name__,
          self.inference_transform,
      )
  
file_path: torch-points3d/torch_points3d/core/data_transform/inference_transforms.py
imports: import os
  import sys
  import logging
label_desc: Use image node_id 3 for calling the PointNetForward obj's underlying member method code with example usage: obj.__repr__() and returns: str