class_docstr: A global system that helps to manage the experiments.
class_docstr_tok: ["A","global","system","that","helps","to","manage","the","experiments","."]
docstr: Get a pandas DataFrame of records that fit the search criteria.
  
  The arguments of this function are not set to be rigid, and they will be different with different implementation of
  ``ExpManager`` in ``Qlib``. ``Qlib`` now provides an implementation of ``ExpManager`` with mlflow, and here is the
  example code of the method with the ``MLflowExpManager``:
  
  .. code-block:: Python
  
      R.log_metrics(m=2.50, step=0)
      records = R.search_records([experiment_id], order_by=["metrics.m DESC"])
  
  Parameters
  ----------
  experiment_ids : list
      list of experiment IDs.
  filter_string : str
      filter query string, defaults to searching all runs.
  run_view_type : int
      one of enum values ACTIVE_ONLY, DELETED_ONLY, or ALL (e.g. in mlflow.entities.ViewType).
  max_results  : int
      the maximum number of runs to put in the dataframe.
  order_by : list
      list of columns to order by (e.g., “metrics.rmse”).
  
  Returns
  -------
  A pandas.DataFrame of records, where each metric, parameter, and tag
  are expanded into their own columns named metrics.*, params.*, and tags.*
  respectively. For records that don't have a particular metric, parameter, or tag, their
  value will be (NumPy) Nan, None, or None respectively.
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code: def search_records(self, experiment_ids, **kwargs):
      """
      Get a pandas DataFrame of records that fit the search criteria.
  
      The arguments of this function are not set to be rigid, and they will be different with different implementation of
      ``ExpManager`` in ``Qlib``. ``Qlib`` now provides an implementation of ``ExpManager`` with mlflow, and here is the
      example code of the method with the ``MLflowExpManager``:
  
      .. code-block:: Python
  
          R.log_metrics(m=2.50, step=0)
          records = R.search_records([experiment_id], order_by=["metrics.m DESC"])
  
      Parameters
      ----------
      experiment_ids : list
          list of experiment IDs.
      filter_string : str
          filter query string, defaults to searching all runs.
      run_view_type : int
          one of enum values ACTIVE_ONLY, DELETED_ONLY, or ALL (e.g. in mlflow.entities.ViewType).
      max_results  : int
          the maximum number of runs to put in the dataframe.
      order_by : list
          list of columns to order by (e.g., “metrics.rmse”).
  
      Returns
      -------
      A pandas.DataFrame of records, where each metric, parameter, and tag
      are expanded into their own columns named metrics.*, params.*, and tags.*
      respectively. For records that don't have a particular metric, parameter, or tag, their
      value will be (NumPy) Nan, None, or None respectively.
      """
      return self.exp_manager.search_records(experiment_ids, **kwargs)
  
imports: from contextlib import contextmanager
  from typing import Text, Optional, Any, Dict
  from .expm import ExpManager
  from .exp import Experiment
  from .recorder import Recorder
  from ..utils import Wrapper
  from ..utils.exceptions import RecorderInitializationError
  import sys
label_desc: Use image node_id 6 for calling the QlibRecorder obj's underlying member method code with example usage: obj.search_records(experiment_ids) and returns: self