class_docstr: Class to analyse input that comes to FEDOT API: input data and params
  All methods are inplace to prevent unnecessary copy of large datasets
  It functionality is:
  1) Cut large datasets to prevent memory stackoverflow
  2) Use label encoder with tree models instead OneHot when summary cardinality of categorical features is high
  3) Give recommendations according to meta rules for more successful optimization process
class_docstr_tok: ["Class","to","analyse","input","that","comes","to","FEDOT","API",":","input","data","and","params","All","methods","are","inplace","to","prevent","unnecessary","copy","of","large","datasets","It","functionality","is",":","1",")","Cut","large","datasets","to","prevent","memory","stackoverflow","2",")","Use","label","encoder","with","tree","models","instead","OneHot","when","summary","cardinality","of","categorical","features","is","high","3",")","Give","recommendations","according","to","meta","rules","for","more","successful","optimization","process"]
code: def __init__(self, safe_mode: bool):
      self.safe_mode = safe_mode
      self.max_size = 50000000
      self.max_cat_cardinality = 50
      self._log = default_log("InputAnalyzer")
  
file_path: FEDOT/fedot/api/api_utils/input_analyser.py
imports: from functools import partial
  from inspect import signature
  from typing import Any, Dict, Tuple, Union
  import numpy
  from golem.core.log import default_log
  from fedot.core.composer.meta_rules import get_cv_folds_number, get_early_stopping_generations, get_recommended_preset
  from fedot.core.data.data import InputData
  from fedot.core.data.data_preprocessing import find_categorical_columns
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 1 to create a new InputAnalyser object  with example: obj = InputAnalyser(safe_mode)