class_docstr: Generate a profile report from a Dataset stored as a pandas `DataFrame`.
  
  Used as is, it will output its content as an HTML report in a Jupyter notebook.
class_docstr_tok: ["Generate","a","profile","report","from","a","Dataset","stored","as","a","pandas","`","DataFrame","`",".","Used","as","is",",","it","will","output","its","content","as","an","HTML","report","in","a","Jupyter","notebook","."]
code: def __validate_inputs(
      df: Optional[Union[pd.DataFrame, sDataFrame]],
      minimal: bool,
      tsmode: bool,
      config_file: Optional[Union[Path, str]],
      lazy: bool,
  ) -> None:
      # Lazy profile cannot be set if no DataFrame is provided
      if df is None and not lazy:
          raise ValueError(
              "Can init a not-lazy ProfileReport with no DataFrame"
          )
  
      if config_file is not None and minimal:
          raise ValueError(
              "Arguments `config_file` and `minimal` are mutually exclusive."
          )
  
      # Spark Dataframe validations
      if isinstance(df, pd.DataFrame):
          if df is not None and df.empty:
              raise ValueError(
                  "DataFrame is empty. Please"
                  "provide a non-empty DataFrame."
              )
      else:
          if tsmode:
              raise NotImplementedError(
                  "Time-Series dataset analysis is not yet supported for Spark DataFrames"
              )
  
          if (
              df is not None and df.rdd.isEmpty()
          ):  # df.isEmpty is only support by 3.3.0 pyspark version
              raise ValueError(
                  "DataFrame is empty. Please"
                  "provide a non-empty DataFrame."
              )
  
imports: import copy
  import json
  import warnings
  from pathlib import Path
  from typing import Any, Optional, Union
  from dataclasses import asdict, is_dataclass
  import numpy
  import pandas
  from tqdm.auto import tqdm
  from typeguard import typechecked
  from visions import VisionsTypeset
  from ydata_profiling.config import Config, Settings, SparkSettings
  from ydata_profiling.expectations_report import ExpectationsReport
  from ydata_profiling.model import BaseDescription
  from ydata_profiling.model.alerts import AlertType
  from ydata_profiling.model.describe import describe
  from ydata_profiling.model.sample import Sample
  from ydata_profiling.model.summarizer import BaseSummarizer, PandasProfilingSummarizer, format_summary, redact_summary
  from ydata_profiling.model.typeset import ProfilingTypeSet
  from ydata_profiling.report import get_report_structure
  from ydata_profiling.report.presentation.core import Root
  from ydata_profiling.report.presentation.flavours.html.templates import create_html_assets
  from ydata_profiling.serialize_report import SerializeReport
  from ydata_profiling.utils.dataframe import hash_dataframe
  from ydata_profiling.utils.paths import get_config
label_desc: Use image node_id 2 for calling the ProfileReport obj's underlying member method code with example usage: obj.__validate_inputs(df, minimal, tsmode, config_file, lazy) without return types