code: def from_files(
      cls,
      predict_files: Optional[List[str]] = None,
      scans_folder_name: Optional[str] = "scans",
      labels_folder_name: Optional[str] = "labels",
      calibrations_folder_name: Optional[str] = "calibs",
      data_format: Optional[
          BaseDataFormat
      ] = PointCloudObjectDetectionDataFormat.KITTI,
      input_cls: Type[Input] = PointCloudObjectDetectorFoldersInput,
      transform: INPUT_TRANSFORM_TYPE = InputTransform,
      transform_kwargs: Optional[Dict] = None,
      **data_module_kwargs: Any,
  ) -> "PointCloudObjectDetectorData":
      ds_kw = {
          "scans_folder_name": scans_folder_name,
          "labels_folder_name": labels_folder_name,
          "calibrations_folder_name": calibrations_folder_name,
          "data_format": data_format,
      }
  
      return cls(
          predict_input=input_cls(
              RunningStage.PREDICTING, predict_files, **ds_kw
          ),
          transform=transform,
          transform_kwargs=transform_kwargs,
          **data_module_kwargs,
      )
  
file_path: lightning-flash/src/flash/pointcloud/detection/data.py
imports: from typing import Any, Dict, List, Optional, Type
  from torch.utils.data import Dataset
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import BaseDataFormat, Input
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.utilities.stability import beta
  from flash.core.utilities.stages import RunningStage
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
  from flash.pointcloud.detection.input import PointCloudObjectDetectorDatasetInput
  from flash.pointcloud.detection.open3d_ml.input import PointCloudObjectDetectionDataFormat, PointCloudObjectDetectorFoldersInput
label_desc: Use image node_id 2 for calling the PointCloudObjectDetectorData obj's underlying member method code with example usage: obj.from_files(cls, predict_files, scans_folder_name, labels_folder_name, calibrations_folder_name, data_format, input_cls, transform, transform_kwargs) and returns: cls