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","."]
docstr: Log a set of metrics. A metric is either a scalar or a set of scalars that will be plotted together
docstr_tok: ["Log","a","set","of","metrics",".","A","metric","is","either","a","scalar","or","a","set","of","scalars","that","will","be","plotted","together"]
code: def log_metrics(
      self,
      metrics: Dict[
          str,
          Union[
              float, torch.Tensor, Dict[str, Union[float, torch.Tensor]]
          ],
      ],
      step: Optional[int] = None,
  ) -> None:
      """Log a set of metrics. A metric is either a scalar or a set of scalars that will be plotted together"""
      super().log_metrics(metrics, step)
      LocalCacheLogger.store_metrics(self, metrics, step)
  
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 2 for calling the OssTensorboardLogger obj's underlying member method code with example usage: obj.log_metrics(metrics, step) without return types