class_docstr: The meta-model for meta-learning-based data selection.
class_docstr_tok: ["The","meta-model","for","meta-learning-based","data","selection","."]
code: def __init__(
      self,
      step,
      hist_step_n,
      clip_method="tanh",
      clip_weight=2.0,
      criterion="ic_loss",
      lr=0.0001,
      max_epoch=100,
      seed=43,
      alpha=0.0,
  ):
      self.step = step
      self.hist_step_n = hist_step_n
      self.clip_method = clip_method
      self.clip_weight = clip_weight
      self.criterion = criterion
      self.lr = lr
      self.max_epoch = max_epoch
      self.fitted = False
      self.alpha = alpha
      torch.manual_seed(seed)
  
imports: import pandas
  import numpy
  import torch
  from torch import nn
  from torch import optim
  from tqdm.auto import tqdm
  import copy
  from typing import Union, List
  from ....model.meta.dataset import MetaTaskDataset
  from ....model.meta.model import MetaTaskModel
  from ....workflow import R
  from .utils import ICLoss
  from .dataset import MetaDatasetDS
  from qlib.log import get_module_logger
  from qlib.model.meta.task import MetaTask
  from qlib.data.dataset.weight import Reweighter
  from qlib.contrib.meta.data_selection.net import PredNet
label_desc: Use image node_id 1 to create a new MetaModelDS object from inherited base classes: MetaTaskModel with example: obj = MetaModelDS(step, hist_step_n, clip_method, clip_weight, criterion, lr, max_epoch, seed, alpha)