class_docstr: Base class for experiment loggers.
docstr: Records metrics. This method logs metrics as soon as it received them.
  
  Args:
      metrics: Dictionary with metric names as keys and measured quantities as values
      step: Step number at which the metrics should be recorded
docstr_tok: ["Records","metrics",".","This","method","logs","metrics","as","soon","as","it","received","them",".","Args",":","metrics",":","Dictionary","with","metric","names","as","keys","and","measured","quantities","as","values","step",":","Step","number","at","which","the","metrics","should","be","recorded"]
code: def log_metrics(
      self, metrics: Dict[str, float], step: Optional[int] = None
  ) -> None:
      """Records metrics. This method logs metrics as soon as it received them.
  
      Args:
          metrics: Dictionary with metric names as keys and measured quantities as values
          step: Step number at which the metrics should be recorded
  
      """
      pass
  
imports: from abc import ABC, abstractmethod
  from argparse import Namespace
  from functools import wraps
  from typing import Any, Callable, Dict, Optional, Union
  from torch import Tensor
  from torch.nn import Module
  from lightning.fabric.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 6 for calling the Logger obj's underlying member method code with example usage: obj.log_metrics(metrics, step) without return types