code: def reset_parameters(self):
      nn.init.kaiming_normal_(self._weight, a=math.sqrt(5))
      fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._weight)
      bound = 4 / math.sqrt(fan_in)
      nn.init.uniform_(self._bias, -bound, bound)
      if self.over_param:
          with torch.no_grad():
              self._bias.set_(self.manifold.expmap0(self._bias))
  
imports: import logging
  import math
  import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import AutoModel, PoincareVAE, PoincareVAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.models.pvae.pvae_utils import PoincareBall
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 4 for calling the RiemannianLayer obj's underlying member method code with example usage: obj.reset_parameters() without return types