code: def __init__(
      self,
      dataset_name: str,
      description: Optional[str] = None,
      df: Optional[pd.DataFrame] = None,
      path: Optional[str] = None,
      meaning_types: Optional[Dict[str, FileColumnMeaningType]] = None,
      search_keys: Optional[List[Tuple[str, ...]]] = None,
      model_task_type: Optional[ModelTaskType] = None,
      random_state: Optional[int] = None,
      endpoint: Optional[str] = None,
      api_key: Optional[str] = None,
      logger: Optional[logging.Logger] = None,
      client_ip: Optional[str] = None,
      warning_counter: Optional[WarningCounter] = None,
      **kwargs,
  ):
      if df is not None:
          data = df.copy()
      elif path is not None:
          if "sep" in kwargs:
              data = pd.read_csv(path, **kwargs)
          else:
              # try different separators: , ; \t ...
              with open(path, mode="r") as csvfile:
                  sep = (
                      csv.Sniffer().sniff(csvfile.read(2048)).delimiter
                  )
              kwargs["sep"] = sep
              data = pd.read_csv(path, **kwargs)
      else:
          raise ValueError(
              bundle.get("dataset_dataframe_or_path_empty")
          )
      if isinstance(data, pd.DataFrame):
          self.data = data
      elif isinstance(data, pd.io.parsers.TextFileReader):  # type: ignore
          raise ValueError(bundle.get("dataset_dataframe_iterator"))
      else:
          raise ValueError(bundle.get("dataset_dataframe_not_pandas"))
  
      self.dataset_name = dataset_name
      self.task_type = model_task_type
      self.description = description
      self.meaning_types = meaning_types
      self.search_keys = search_keys
      self.ignore_columns = []
      self.hierarchical_group_keys = []
      self.hierarchical_subgroup_keys = []
      self.file_upload_id: Optional[str] = None
      self.etalon_def: Optional[Dict[str, str]] = None
      self.endpoint = endpoint
      self.api_key = api_key
      self.random_state = random_state
      self.columns_renaming: Dict[str, str] = {}
      self.imbalanced: bool = False
      if logger is not None:
          self.logger = logger
      else:
          self.logger = logging.getLogger()
          self.logger.setLevel("FATAL")
      self.client_ip = client_ip
      self.warning_counter = warning_counter or WarningCounter()
  
imports: import csv
  import hashlib
  import logging
  import tempfile
  import time
  from ipaddress import IPv4Address, IPv6Address, _BaseAddress, ip_address
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from pandas.api.types import is_bool_dtype
  from pandas.api.types import is_datetime64_any_dtype
  from pandas.api.types import is_float_dtype, is_integer_dtype, is_numeric_dtype, is_string_dtype
  from pandas.core.dtypes.common import is_period_dtype
  from upgini.errors import ValidationError
  from upgini.http import ProgressStage, SearchProgress, get_rest_client
  from upgini.metadata import EVAL_SET_INDEX, SYSTEM_COLUMNS, SYSTEM_RECORD_ID, TARGET, DataType, FeaturesFilter, FileColumnMeaningType, FileColumnMetadata, FileMetadata, FileMetrics, ModelTaskType, NumericInterval, RuntimeParameters, SearchCustomization
  from upgini.normalizer.phone_normalizer import PhoneNormalizer
  from upgini.resource_bundle import bundle
  from upgini.sampler.random_under_sampler import RandomUnderSampler
  from upgini.search_task import SearchTask
  from upgini.utils import combine_search_keys
  from upgini.utils.email_utils import EmailSearchKeyConverter
  from upgini.utils.warning_counter import WarningCounter
label_desc: Use image node_id 1 to create a new Dataset object  with example: obj = Dataset(dataset_name, description, df, path, meaning_types, search_keys, model_task_type, random_state, endpoint, api_key, logger, client_ip, warning_counter)