class_docstr: A single meta-task, a meta-dataset contains a list of them.
  It serves as a component as in MetaDatasetDS
  
  The data processing is different
  
  - the processed input may be different between training and testing
  
      - When training, the X, y, X_test, y_test in training tasks are necessary (# PROC_MODE_FULL #)
        but not necessary in test tasks. (# PROC_MODE_TEST #)
      - When the meta model can be transferred into other dataset, only meta_info is necessary  (# PROC_MODE_TRANSFER #)
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docstr: The `__init__` func is responsible for
  
  - store the task
  - store the origin input data for
  - process the input data for meta data
  
  Parameters
  ----------
  task : dict
      the task to be enhanced by meta model
  
  meta_info : object
      the input for meta model
docstr_tok: ["The","`","__init__","`","func","is","responsible","for","-","store","the","task","-","store","the","origin","input","data","for","-","process","the","input","data","for","meta","data","Parameters","--","--","--","--","--","task",":","dict","the","task","to","be","enhanced","by","meta","model","meta_info",":","object","the","input","for","meta","model"]
code: def __init__(
      self, task: dict, meta_info: object, mode: str = PROC_MODE_FULL
  ):
      """
      The `__init__` func is responsible for
  
      - store the task
      - store the origin input data for
      - process the input data for meta data
  
      Parameters
      ----------
      task : dict
          the task to be enhanced by meta model
  
      meta_info : object
          the input for meta model
      """
      self.task = task
      self.meta_info = meta_info  # the original meta input information, it will be processed later
      self.mode = mode
  
imports: from qlib.data.dataset import Dataset
  from ...utils import init_instance_by_config
label_desc: Use image node_id 1 to create a new MetaTask object  with example: obj = MetaTask(task, meta_info, mode)