docstr: Parameters
  ----------
  alpha : float
      the regularization for sub model (useful when align meta model with linear submodel)
docstr_tok: ["Parameters","--","--","--","--","--","alpha",":","float","the","regularization","for","sub","model","(","useful","when","align","meta","model","with","linear","submodel",")"]
code: def __init__(
      self,
      step,
      hist_step_n,
      clip_weight=None,
      clip_method="tanh",
      alpha: float = 0.0,
  ):
      """
      Parameters
      ----------
      alpha : float
          the regularization for sub model (useful when align meta model with linear submodel)
      """
      super().__init__()
      self.step = step
      self.twm = TimeWeightMeta(
          hist_step_n=hist_step_n,
          clip_weight=clip_weight,
          clip_method=clip_method,
      )
      self.init_paramters(hist_step_n)
      self.alpha = alpha
  
file_path: qlib/qlib/contrib/meta/data_selection/net.py
imports: import numpy
  import torch
  from torch import nn
  from .utils import preds_to_weight_with_clamp, SingleMetaBase
label_desc: Use image node_id 1 to create a new PredNet object from inherited base classes: nn with example: obj = PredNet(step, hist_step_n, clip_weight, clip_method, alpha)