class_docstr: Concatenating embedding with float features before passing the input
  to DQN
code: def input_prototype(self):
      id_list_features = {
          k: (
              torch.tensor([0], dtype=torch.long),
              torch.tensor([], dtype=torch.long),
          )
          for k in self._id_list_feature_names
      }
      id_score_list_features = {
          k: (
              torch.tensor([0], dtype=torch.long),
              torch.tensor([], dtype=torch.long),
              torch.tensor([], dtype=torch.float),
          )
          for k in self._id_score_list_feature_names
      }
      return rlt.FeatureData(
          float_features=torch.randn(1, self.state_dense_dim),
          id_list_features_raw=id_list_features,
          id_score_list_features_raw=id_score_list_features,
      )
  
file_path: ReAgent/reagent/models/embedding_bag_concat.py
imports: from typing import Dict, List
  import torch
  from reagent.core import types
  from reagent.core.utils import embedding_bag_configs_from_feature_configs
  from reagent.models.base import ModelBase
  from torchrec import EmbeddingBagConfig
label_desc: Use image node_id 3 for calling the EmbeddingBagConcat obj's underlying member method code with example usage: obj.input_prototype() and returns: rlt