code: def _init_params_from_layermapping(
      self, weight_maps, bias_maps, random_seed=None
  ):
      rgen = np.random.RandomState(random_seed)
      weights, biases = {}, {}
  
      rgen = np.random.RandomState(random_seed)
      weights, biases = {}, {}
  
      for kw, kb in zip(sorted(weight_maps), sorted(bias_maps)):
          weights[kw] = rgen.normal(
              loc=0.0, scale=0.01, size=weight_maps[kw][0]
          )
          biases[kb] = np.zeros(shape=bias_maps[kb][0])
      return weights, biases
  
imports: import numpy
label_desc: Use image node_id 3 for calling the _MultiLayer obj's underlying member method code with example usage: obj._init_params_from_layermapping(weight_maps, bias_maps, random_seed) and returns: weights,  biases