code: def test_batch_metrics_logger_runs_training_and_logging_in_correct_ratio(
      start_run,
  ):
      with mock.patch.object(
          MlflowClient, "log_batch"
      ) as log_batch_mock:
          run_id = mlflow.active_run().info.run_id
          with batch_metrics_logger(run_id) as metrics_logger:
              metrics_logger.record_metrics(
                  {"x": 1}, step=0
              )  # data doesn't matter
  
              # first metrics should be logged immediately to record a previous timestamp and
              #   batch log time
              log_batch_mock.assert_called_once()
  
              metrics_logger.total_log_batch_time = 1
              metrics_logger.total_training_time = 1
  
              log_batch_mock.reset_mock()  # resets the 'calls' of this mock
  
              # the above 'training' took 1 second. So with target training-to-logging time ratio of
              #   10:1, 9 more 'training' should happen without sending the batch and then after the
              #   10th training the batch should be sent.
              for i in range(2, 11):
                  metrics_logger.record_metrics({"x": 1}, step=0)
                  log_batch_mock.assert_not_called()
                  metrics_logger.total_training_time = i
  
              # at this point, average log batch time is 1, and total training time is 9
              # thus the next record_metrics call should send the batch.
              metrics_logger.record_metrics({"x": 1}, step=0)
              log_batch_mock.assert_called_once()
  
              # update log_batch time to reflect the 'mocked' training time
              metrics_logger.total_log_batch_time = 2
  
              log_batch_mock.reset_mock()  # reset the recorded calls
  
              for i in range(12, 21):
                  metrics_logger.record_metrics({"x": 1}, step=0)
                  log_batch_mock.assert_not_called()
                  metrics_logger.total_training_time = i
  
              metrics_logger.record_metrics({"x": 1}, step=0)
              log_batch_mock.assert_called_once()
  
imports: import inspect
  import sys
  import time
  from collections import namedtuple
  from unittest import mock
  import pytest
  import mlflow
  from mlflow import MlflowClient
  from mlflow.utils import gorilla
  from mlflow.utils.autologging_utils import AUTOLOGGING_INTEGRATIONS, AutologgingEventLogger, BatchMetricsLogger, autologging_integration, autologging_is_disabled, batch_metrics_logger, get_autologging_config, get_instance_method_first_arg_value, get_method_call_arg_value, log_fn_args_as_params, resolve_input_example_and_signature
  from mlflow.utils.autologging_utils.safety import AutologgingSession, _wrap_patch
  from mlflow.utils.autologging_utils.versioning import FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY, _check_version_in_range, _is_pre_or_dev_release, _strip_dev_version_suffix, _violates_pep_440, is_flavor_supported_for_associated_package_versions
  from tests.autologging.fixtures import test_mode_off
label_desc: Use image node_id 19 for calling a global function with example usage: test_batch_metrics_logger_runs_training_and_logging_in_correct_ratio(start_run) without return types