class_docstr: Generates dataset summary including counts, number of unique elements, most frequent, dtypes and 7-figure summary (std/mean/min/max/quartiles)
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>> import autogluon.eda.visualization as viz
  >>> import autogluon.eda.auto as auto
  >>> state = auto.analyze(
  >>>     train_data=..., label=..., return_state=True,
  >>>     anlz_facets=[
  >>>         eda.dataset.DatasetSummary(),
  >>>     ],
  >>>     viz_facets=[
  >>>         viz.dataset.DatasetStatistics()
  >>>     ]
  >>> )
  
  See Also
  --------
  :py:class:`~autogluon.eda.visualization.dataset.DatasetStatistics`
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code: def can_handle(
      self, state: AnalysisState, args: AnalysisState
  ) -> bool:
      return True
  
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 1 for calling the DatasetSummary obj's underlying member method code with example usage: obj.can_handle(state, args) and returns: True