class_docstr: Base for provider of data for one scene from LLFF or blender datasets.
  
  Members:
      base_dir: directory holding the data for the scene.
      object_name: The name of the scene (e.g. "lego"). This is just used as a label.
          It will typically be equal to the name of the directory self.base_dir.
      path_manager_factory: Creates path manager which may be used for
          interpreting paths.
      n_known_frames_for_test: If set, training frames are included in the val
          and test datasets, and this many random training frames are added to
          each test batch. If not set, test batches each contain just a single
          testing frame.
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code: def get_dataset_map(self) -> DatasetMap:
      return DatasetMap(
          train=self._get_dataset(0, DATASET_TYPE_KNOWN),
          val=self._get_dataset(1, DATASET_TYPE_UNKNOWN),
          test=self._get_dataset(2, DATASET_TYPE_UNKNOWN, True),
      )
  
imports: from dataclasses import field
  from typing import Iterable, Iterator, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from pytorch3d.implicitron.tools.config import Configurable, expand_args_fields, run_auto_creation
  from pytorch3d.renderer import CamerasBase, join_cameras_as_batch, PerspectiveCameras
  from .dataset_base import DatasetBase
  from .dataset_map_provider import DatasetMap, DatasetMapProviderBase, PathManagerFactory
  from .frame_data import FrameData
  from .utils import DATASET_TYPE_KNOWN, DATASET_TYPE_UNKNOWN
label_desc: Use image node_id 4 for calling the SingleSceneDatasetMapProviderBase obj's underlying member method code with example usage: obj.get_dataset_map() and returns: DatasetMap