code: def test_mlflow_logger_with_many_params(mlflow_mock, tmp_path):
      """Test that when logging more than 100 parameters, it will be split into batches of at most 100 parameters."""
      logger = MLFlowLogger("test", save_dir=str(tmp_path))
  
      params = {
          f"test_{idx}": f"test_param_{idx}" for idx in range(150)
      }
      logger.log_hyperparams(params)
  
      assert logger.experiment.log_batch.call_count == 2
  
imports: import os
  from unittest import mock
  from unittest.mock import MagicMock, Mock
  import pytest
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.loggers.mlflow import _MLFLOW_AVAILABLE, MLFlowLogger, _get_resolve_tags
label_desc: Use image node_id 11 for calling a global function with example usage: test_mlflow_logger_with_many_params(mlflow_mock, tmp_path) without return types