code: def __init__(
      self,
      data,
      cache_dir="./logs/cache",
      use_cache=False,
      num_points=65536,
      ignored_label_inds=[0],
      predicting=False,
      **kwargs,
  ):
      super().__init__()
  
      self.name = "Dataset"
      self.ignored_label_inds = ignored_label_inds
  
      kwargs["cache_dir"] = cache_dir
      kwargs["use_cache"] = use_cache
      kwargs["num_points"] = num_points
      kwargs["ignored_label_inds"] = ignored_label_inds
  
      self.cfg = Config(kwargs)
      self.predicting = predicting
  
      if not predicting:
          self.on_fit(data)
      else:
          self.on_predict(data)
  
file_path: lightning-flash/src/flash/pointcloud/segmentation/open3d_ml/sequences_dataset.py
imports: import os
  from os.path import basename, dirname, exists, isdir, isfile, join, split
  import numpy
  import yaml
  from torch.utils.data import Dataset
  from flash.core.utilities.imports import _TOPIC_POINTCLOUD_AVAILABLE
label_desc: Use image node_id 1 to create a new SequencesDataset object from inherited base classes: Dataset with example: obj = SequencesDataset(data, cache_dir, use_cache, num_points, ignored_label_inds, predicting)