code: def define_data(
      self,
      features: Union[str, PathLike],
      task: Task,
      target: str = None,
      is_predict: bool = False,
  ) -> InputData:
      # CSV files as input data, by default - table data
  
      if task.task_type == TaskTypesEnum.ts_forecasting:
          # For time series forecasting format - time series
          data = InputData.from_csv_time_series(
              task=task,
              file_path=features,
              target_column=target,
              is_predict=is_predict,
          )
      else:
          # Make default features table
          # CSV files as input data
          data = InputData.from_csv(
              features,
              task=task,
              target_columns=target,
              data_type=DataTypesEnum.table,
          )
      return data
  
imports: from abc import ABC, abstractmethod
  from copy import deepcopy
  from os import PathLike
  from typing import Union, Optional
  import numpy
  import pandas
  from fedot.core.data.data import InputData, array_to_input_data
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 1 for calling the CsvStrategy obj's underlying member method code with example usage: obj.define_data(features, task, target, is_predict) and returns: data