code: def _init_params(self):
      # Initialize parameters with Glorot / fan_avg.
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
  
      def _num_of_params(model):
          return len(
              torch.cat([p.flatten() for p in model.parameters()])
          )
  
      logger.info(
          f"Num of total params: {_num_of_params(self)}, {self._get_name()}"
      )
  
imports: import copy
  import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import types
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import subsequent_mask
  from reagent.models.base import ModelBase
  from reagent.models.seq2slate import Decoder, DecoderLayer, Embedder, Encoder, EncoderLayer, MultiHeadedAttention, PositionalEncoding, PositionwiseFeedForward
label_desc: Use image node_id 3 for calling the Seq2SlateRewardNetBase obj's underlying member method code with example usage: obj._init_params() and returns: len