class_docstr: Abstract base class for all data formatters.
  
  User can implement the abstract methods below to perform dataset-specific
  manipulations.
class_docstr_tok: ["Abstract","base","class","for","all","data","formatters",".","User","can","implement","the","abstract","methods","below","to","perform","dataset-specific","manipulations","."]
docstr: Defines the fixed parameters used by the model for training.
  
  Requires the following keys:
    'total_time_steps': Defines the total number of time steps used by TFT
    'num_encoder_steps': Determines length of LSTM encoder (i.e. history)
    'num_epochs': Maximum number of epochs for training
    'early_stopping_patience': Early stopping param for keras
    'multiprocessing_workers': # of cpus for data processing
  
  
  Returns:
    A dictionary of fixed parameters, e.g.:
  
    fixed_params = {
        'total_time_steps': 252 + 5,
        'num_encoder_steps': 252,
        'num_epochs': 100,
        'early_stopping_patience': 5,
        'multiprocessing_workers': 5,
    }
docstr_tok: ["Defines","the","fixed","parameters","used","by","the","model","for","training",".","Requires","the","following","keys",":","'total_time_steps","'",":","Defines","the","total","number","of","time","steps","used","by","TFT","'num_encoder_steps","'",":","Determines","length","of","LSTM","encoder","(","i.e",".","history",")","'num_epochs","'",":","Maximum","number","of","epochs","for","training","'early_stopping_patience","'",":","Early","stopping","param","for","keras","'multiprocessing_workers","'",":","#","of","cpus","for","data","processing","Returns",":","A","dictionary","of","fixed","parameters",",","e.g",".",":","fixed_params","=","{","'total_time_steps","'",":","252","+","5",",","'num_encoder_steps","'",":","252",",","'num_epochs","'",":","100",",","'early_stopping_patience","'",":","5",",","'multiprocessing_workers","'",":","5",",","}"]
code: def get_fixed_params(self):
      """Defines the fixed parameters used by the model for training.
  
      Requires the following keys:
        'total_time_steps': Defines the total number of time steps used by TFT
        'num_encoder_steps': Determines length of LSTM encoder (i.e. history)
        'num_epochs': Maximum number of epochs for training
        'early_stopping_patience': Early stopping param for keras
        'multiprocessing_workers': # of cpus for data processing
  
  
      Returns:
        A dictionary of fixed parameters, e.g.:
  
        fixed_params = {
            'total_time_steps': 252 + 5,
            'num_encoder_steps': 252,
            'num_epochs': 100,
            'early_stopping_patience': 5,
            'multiprocessing_workers': 5,
        }
      """
      raise NotImplementedError
  
file_path: qlib/examples/benchmarks/TFT/data_formatters/base.py
imports: import abc
  import enum
label_desc: Use image node_id 6 for calling the GenericDataFormatter obj's underlying member method code with example usage: obj.get_fixed_params() without return types