code: def _build_embeddings(self, model):
      with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
          sqrt3 = math.sqrt(3)
          self.encoder_embeddings = model.param_init_net.UniformFill(
              [],
              "encoder_embeddings",
              shape=[
                  self.source_vocab_size,
                  self.model_params["encoder_embedding_size"],
              ],
              min=-sqrt3,
              max=sqrt3,
          )
          model.params.append(self.encoder_embeddings)
          self.decoder_embeddings = model.param_init_net.UniformFill(
              [],
              "decoder_embeddings",
              shape=[
                  self.target_vocab_size,
                  self.model_params["decoder_embedding_size"],
              ],
              min=-sqrt3,
              max=sqrt3,
          )
          model.params.append(self.decoder_embeddings)
  
imports: import argparse
  import collections
  import logging
  import math
  import numpy
  import random
  import time
  import sys
  import os
  import caffe2.proto.caffe2_pb2
  from caffe2.python import core, workspace, data_parallel_model
  import caffe2.python.models.seq2seq.seq2seq_util
  from caffe2.python.models.seq2seq.seq2seq_model_helper import Seq2SeqModelHelper
label_desc: Use image node_id 3 for calling the Seq2SeqModelCaffe2 obj's underlying member method code with example usage: obj._build_embeddings(model) without return types