class_docstr: Concatenating embeddings from bag collection with float features before passing the input
  to DQN
class_docstr_tok: ["Concatenating","embeddings","from","bag","collection","with","float","features","before","passing","the","input","to","DQN"]
code: def __init__(
      self,
      state_dense_dim: int,
      embedding_bag_collection: EmbeddingBagCollection,
      action_dense_dim: int,
      overarch_dims: List[int],
      activation: str = "relu",
      final_activation: str = "relu",
      use_batch_norm: bool = True,
      use_layer_norm: bool = False,
      output_dim: int = 1,
  ) -> None:
      super().__init__()
      self.sparse_arch: SparseArch = SparseArch(
          embedding_bag_collection
      )
  
      self.sparse_embedding_dim: int = sum(
          [
              len(embc.feature_names) * embc.embedding_dim
              for embc in embedding_bag_collection.embedding_bag_configs()
          ]
      )
  
      self.input_dim: int = (
          state_dense_dim + self.sparse_embedding_dim + action_dense_dim
      )
      layers = [self.input_dim] + overarch_dims + [output_dim]
      activations = [activation] * len(overarch_dims) + [
          final_activation
      ]
      self.q_network = FullyConnectedNetwork(
          layers=layers,
          activations=activations,
          use_batch_norm=use_batch_norm,
      )
  
file_path: ReAgent/reagent/models/sparse_dqn.py
imports: from typing import List
  import torch
  from reagent.core import types
  from reagent.models import FullyConnectedNetwork
  from reagent.models.base import ModelBase
  from torchrec.models.dlrm import SparseArch
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 to create a new SparseDQN object from inherited base classes: ModelBase with example: obj = SparseDQN(state_dense_dim, embedding_bag_collection, action_dense_dim, overarch_dims, activation, final_activation, use_batch_norm, use_layer_norm, output_dim)