class_docstr: Preparing data for model training and inferencing.
class_docstr_tok: ["Preparing","data","for","model","training","and","inferencing","."]
docstr: The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
  The parameters should specify the scope for the prepared data
  The method should:
  - process the data
  
  - return the processed data
  
  Returns
  -------
  object:
      return the object
docstr_tok: ["The","type","of","dataset","depends","on","the","model",".","(","It","could","be","pd.DataFrame",",","pytorch.DataLoader",",","etc",".",")","The","parameters","should","specify","the","scope","for","the","prepared","data","The","method","should",":","-","process","the","data","-","return","the","processed","data","Returns","--","--","--","-","object",":","return","the","object"]
code: def prepare(self, **kwargs) -> object:
      """
      The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
      The parameters should specify the scope for the prepared data
      The method should:
      - process the data
  
      - return the processed data
  
      Returns
      -------
      object:
          return the object
      """
  
imports: from ...utils.serial import Serializable
  from typing import Callable, Union, List, Tuple, Dict, Text, Optional
  from ...utils import init_instance_by_config, np_ffill, time_to_slc_point
  from ...log import get_module_logger
  from .handler import DataHandler, DataHandlerLP
  from copy import copy, deepcopy
  from inspect import getfullargspec
  import pandas
  import numpy
  import bisect
  from ...utils import lazy_sort_index
  from .utils import get_level_index
label_desc: Use image node_id 4 for calling the Dataset obj's underlying member method code with example usage: obj.prepare() without return types