class_docstr: Base class to describe a dataset to be used with Implicitron.
  
  The dataset is made up of frames, and the frames are grouped into sequences.
  Each sequence has a name (a string).
  (A sequence could be a video, or a set of images of one scene.)
  
  This means they have a __getitem__ which returns an instance of a FrameData,
  which will describe one frame in one sequence.
class_docstr_tok: ["Base","class","to","describe","a","dataset","to","be","used","with","Implicitron",".","The","dataset","is","made","up","of","frames",",","and","the","frames","are","grouped","into","sequences",".","Each","sequence","has","a","name","(","a","string",")",".","(","A","sequence","could","be","a","video",",","or","a","set","of","images","of","one","scene",".",")","This","means","they","have","a","__getitem__","which","returns","an","instance","of","a","FrameData",",","which","will","describe","one","frame","in","one","sequence","."]
docstr: Returns an iterator over sequence names in the dataset.
docstr_tok: ["Returns","an","iterator","over","sequence","names","in","the","dataset","."]
code: def sequence_names(self) -> Iterable[str]:
      """Returns an iterator over sequence names in the dataset."""
      # pyre-ignore[16]
      return self._seq_to_idx.keys()
  
file_path: pytorch3d/pytorch3d/implicitron/dataset/dataset_base.py
imports: from collections import defaultdict
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Iterable, Iterator, List, Optional, Sequence, Tuple, Type
  import torch
  from pytorch3d.implicitron.dataset.frame_data import FrameData
  from pytorch3d.implicitron.dataset.utils import GenericWorkaround
label_desc: Use image node_id 5 for calling the DatasetBase obj's underlying member method code with example usage: obj.sequence_names() and returns: self