code: def normalize_filter(bs: Dict, ws: Dict):
      bs = {k: v.float() for k, v in bs.items()}
      ws = {k: v.float() for k, v in ws.items()}
  
      norm_bs = {}
      for k in bs:
          ws_norm = torch.norm(ws[k], dim=0, keepdim=True)
          bs_norm = torch.norm(bs[k], dim=0, keepdim=True)
          norm_bs[k] = ws_norm / (bs_norm + 1e-7) * bs[k]
  
      return norm_bs
  
file_path: ml-cvnets/loss_landscape/landscape_utils.py
imports: import copy
  from typing import Dict, Optional
  import matplotlib.pyplot
  import numpy
  import torch
  from matplotlib import animation, cm
  from mpl_toolkits.mplot3d import Axes3D
  from utils.common_utils import unwrap_model_fn
label_desc: Use image node_id 2 for calling a global function with example usage: normalize_filter(bs, ws) and returns: norm_bs