docstr: Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used
  to create Block when VectorEmbedder is initialised.
  
  :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block.
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code: def schemes(self):
      """
      Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used
      to create Block when VectorEmbedder is initialised.
  
      :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block.
      """
      return {
          EmbedderScheme.Empty: [],
          EmbedderScheme.Shallow: [Dense(units=128)],
          # Use for DQN
          EmbedderScheme.Medium: [Dense(units=256)],
          # Use for Carla
          EmbedderScheme.Deep: [
              Dense(units=128),
              Dense(units=128),
              Dense(units=128),
          ],
      }
  
file_path: coach/rl_coach/architectures/mxnet_components/embedders/vector_embedder.py
imports: from typing import Union
  from types import ModuleType
  import mxnet
  from mxnet import nd, sym
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.mxnet_components.embedders.embedder import InputEmbedder
  from rl_coach.architectures.mxnet_components.layers import Dense
  from rl_coach.base_parameters import EmbedderScheme
label_desc: Use image node_id 2 for calling the VectorEmbedder obj's underlying member method code with example usage: obj.schemes() and returns: dict