class_docstr: Abstract class for Backend Storage.
  This class defines the API interface for front ends to connect with various types of backends.
docstr: Log a metric for the specified run
  
  :param run_id: String id for the run
  :param metric: :py:class:`mlflow.entities.Metric` instance to log
docstr_tok: ["Log","a","metric","for","the","specified","run",":","param","run_id",":","String","id","for","the","run",":","param","metric",":",":","py",":","class",":","`","mlflow.entities.Metric","`","instance","to","log"]
code: def log_metric(self, run_id, metric):
      """
      Log a metric for the specified run
  
      :param run_id: String id for the run
      :param metric: :py:class:`mlflow.entities.Metric` instance to log
      """
      self.log_batch(run_id, metrics=[metric], params=[], tags=[])
  
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Optional
  from mlflow.entities import DatasetInput, ViewType
  from mlflow.store.entities.paged_list import PagedList
  from mlflow.store.tracking import SEARCH_MAX_RESULTS_DEFAULT
  from mlflow.utils.annotations import developer_stable, experimental
  from mlflow.utils.async_logging.async_logging_queue import AsyncLoggingQueue
  from mlflow.utils.async_logging.run_operations import RunOperations
label_desc: Use image node_id 14 for calling the AbstractStore obj's underlying member method code with example usage: obj.log_metric(run_id, metric) without return types