class_docstr:  Analyze label for insights:
  
   - classification: low cardinality classes detection
   - classification: classes present in test data, but not in the train data
   - regression: out-of-domain labels detection
  
   Note: this Analysis requires `problem_type` present in state.
   It can be detected/set via :py:class:`~autogluon.eda.analysis.dataset.ProblemTypeControl` component
  
  Parameters
   ----------
   low_cardinality_classes_threshold: int, default = 50
       Minimum class instances present in the dataset to consider marking a class as low-cardinality
   regression_ood_threshold: float, default = 0.01
       mark results as out-of-domain when test label range in regression task is beyond train data range + regression_ood_threshold margin,
       This is performed because some algorithms can't extrapolate beyond training data.
   class_imbalance_ratio_threshold: float, default = 0.4
      minority class proportion to detect as imbalance.
   parent: Optional[AbstractAnalysis], default = None
       parent Analysis
   children: Optional[List[AbstractAnalysis]], default None
       wrapped analyses; these will receive sampled `args` during `fit` call
   state: AnalysisState
       state object to perform check on
  
   Examples
   --------
   >>> import autogluon.eda.analysis as eda
   >>> import autogluon.eda.visualization as viz
   >>> import autogluon.eda.auto as auto
   >>> auto.analyze(
   >>> auto.analyze(train_data=..., test_data=..., label=..., anlz_facets=[
   >>>     eda.dataset.ProblemTypeControl(),
   >>>     eda.dataset.LabelInsightsAnalysis(low_cardinality_classes_threshold=50, regression_ood_threshold=0.01),
   >>> ], viz_facets=[
   >>>     viz.dataset.LabelInsightsVisualization()
   >>> ])
  
   See Also
   --------
   :py:class:`~autogluon.eda.analysis.dataset.ProblemTypeControl`
   :py:class:`~autogluon.eda.visualization.dataset.LabelInsightsVisualization`
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code: def _test_data_with_label_present(self, args, label):
      return (args.test_data is not None) and (
          label in args.test_data.columns
      )
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Optional, Set, Union
  import numpy
  import pandas
  from autogluon.common.features.infer_types import get_type_group_map_special, get_type_map_raw
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT, R_OBJECT
  from ..state import AnalysisState
  from .base import AbstractAnalysis
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, PROBLEM_TYPES_REGRESSION, REGRESSION
  from autogluon.core.utils import generate_train_test_split_combined, infer_problem_type
label_desc: Use image node_id 4 for calling the LabelInsightsAnalysis obj's underlying member method code with example usage: obj._test_data_with_label_present(args, label) and returns: unknown