class_docstr: This type of meta-model deals with base task definitions. The meta-model creates tasks for training new base forecasting models after it is trained. `prepare_tasks` directly modifies the task definitions.
docstr: MetaTaskModel will make inference on the meta_dataset
  The MetaTaskModel is expected to get prepared MetaTask from meta_dataset.
  Then it will create modified task with Qlib format which can be executed by Qlib trainer.
  
  Returns
  -------
  List[dict]:
      A list of modified task definitions.
docstr_tok: ["MetaTaskModel","will","make","inference","on","the","meta_dataset","The","MetaTaskModel","is","expected","to","get","prepared","MetaTask","from","meta_dataset",".","Then","it","will","create","modified","task","with","Qlib","format","which","can","be","executed","by","Qlib","trainer",".","Returns","--","--","--","-","List","[","dict","]",":","A","list","of","modified","task","definitions","."]
code: def inference(self, meta_dataset: MetaTaskDataset) -> List[dict]:
      """
      MetaTaskModel will make inference on the meta_dataset
      The MetaTaskModel is expected to get prepared MetaTask from meta_dataset.
      Then it will create modified task with Qlib format which can be executed by Qlib trainer.
  
      Returns
      -------
      List[dict]:
          A list of modified task definitions.
  
      """
      raise NotImplementedError(
          f"Please implement the `inference` method"
      )
  
file_path: qlib/qlib/model/meta/model.py
imports: import abc
  from typing import List
  from .dataset import MetaTaskDataset
label_desc: Use image node_id 2 for calling the MetaTaskModel obj's underlying member method code with example usage: obj.inference(meta_dataset) without return types