code: def define_data(
      self,
      features: np.ndarray,
      task: Task,
      target: Optional[int] = None,
      is_predict: bool = False,
  ) -> InputData:
      # numpy format for input data
      if target is None:
          target = np.array([])
  
      if task.task_type is TaskTypesEnum.ts_forecasting:
          target_array = features
      elif isinstance(target, int):
          if target < features.shape[1]:
              target_array = features[:, target]
              features = np.delete(features, target, axis=1)
          else:
              target_array = np.array([])
      else:
          target_array = target
  
      data = array_to_input_data(
          features_array=features, target_array=target_array, task=task
      )
      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 NumpyStrategy obj's underlying member method code with example usage: obj.define_data(features, task, target, is_predict) and returns: data