class_docstr: Wrapper around ManifoldTensorBoardLogger that collects the plot data in memory and can flush to create fblearner plot objects.
class_docstr_tok: ["Wrapper","around","ManifoldTensorBoardLogger","that","collects","the","plot","data","in","memory","and","can","flush","to","create","fblearner","plot","objects","."]
code: def __init__(
      self,
      save_dir: str,
      name: Optional[str] = "default",
      version: Optional[Union[int, str]] = None,
      log_graph: bool = False,
      default_hp_metric: bool = True,
      prefix: str = "",
      **kwargs,
  ) -> None:
      super().__init__(
          save_dir,
          name,
          version,
          log_graph,
          default_hp_metric,
          prefix,
          **kwargs,
      )
      self.line_plot_aggregated: Dict[
          str, Dict[str, List[Tuple[float, float]]]
      ] = {}
      self.line_plot_buffer: Dict[
          str, Dict[str, List[Tuple[float, float]]]
      ] = {}
  
file_path: ReAgent/reagent/core/oss_tensorboard_logger.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from pytorch_lightning.loggers import TensorBoardLogger
  from pytorch_lightning.utilities import rank_zero_only
label_desc: Use image node_id 1 to create a new OssTensorboardLogger object from inherited base classes: TensorBoardLogger with example: obj = OssTensorboardLogger(save_dir, name, version, log_graph, default_hp_metric, prefix)