class_docstr: Meta Task for Data Selection
class_docstr_tok: ["Meta","Task","for","Data","Selection"]
code: def _get_processed_meta_info(self):
      meta_info_norm = self.meta_info.sub(
          self.meta_info.mean(axis=1), axis=0
      )
      if self.fill_method.startswith("max"):
          suffix = self.fill_method.lstrip("max")
          if suffix == "seg":
              fill_value = {}
              for col in meta_info_norm.columns:
                  fill_value[col] = (
                      meta_info_norm.loc[meta_info_norm[col].isna(), :]
                      .dropna(axis=1)
                      .mean()
                      .max()
                  )
              fill_value = pd.Series(fill_value).sort_index()
              # The NaN Values are filled segment-wise. Below is an exampleof fill_value
              # 2009-01-05  2009-02-06    0.145809
              # 2009-02-09  2009-03-06    0.148005
              # 2009-03-09  2009-04-03    0.090385
              # 2009-04-07  2009-05-05    0.114318
              # 2009-05-06  2009-06-04    0.119328
              # ...
              meta_info_norm = meta_info_norm.fillna(fill_value)
          else:
              if len(suffix) > 0:
                  get_module_logger("MetaTaskDS").warning(
                      f"fill_method={self.fill_method}; the info after can't be correctly parsed. Please check your parameters."
                  )
              fill_value = meta_info_norm.max(axis=1)
              # fill it with row max to align with previous implementation
              # This will magnify the data similarity when data is in daily freq
  
              # the fill value corresponds to data like this
              # It get a performance value for each day.
              # The performance value are get from other models on this day
              # 2009-01-16    0.276320
              # 2009-01-19    0.280603
              #                 ...
              # 2011-06-27    0.203773
              meta_info_norm = meta_info_norm.T.fillna(fill_value).T
      elif self.fill_method == "zero":
          # It will fillna(0.0) at the end.
          pass
      else:
          raise NotImplementedError(
              f"This type of input is not supported"
          )
      meta_info_norm = meta_info_norm.fillna(
          0.0
      )  # always fill zero in case of NaN
      return meta_info_norm
  
imports: import pandas
  import numpy
  from copy import deepcopy
  from joblib import Parallel, delayed
  from typing import Dict, List, Union, Text, Tuple
  from qlib.data.dataset.utils import init_task_handler
  from qlib.data.dataset import DatasetH
  from qlib.contrib.torch import data_to_tensor
  from qlib.model.meta.task import MetaTask
  from qlib.model.meta.dataset import MetaTaskDataset
  from qlib.model.trainer import TrainerR
  from qlib.log import get_module_logger
  from qlib.utils import auto_filter_kwargs, get_date_by_shift, init_instance_by_config
  from qlib.utils.data import deepcopy_basic_type
  from qlib.workflow import R
  from qlib.workflow.task.gen import RollingGen, task_generator
  from qlib.workflow.task.utils import TimeAdjuster
  from tqdm.auto import tqdm
label_desc: Use image node_id 2 for calling the MetaTaskDS obj's underlying member method code with example usage: obj._get_processed_meta_info() and returns: meta_info_norm