class_docstr: An input embedder that is intended for inputs that can be represented as vectors.
  The embedder flattens the input, applies several dense layers to it and returns the output.
code: def schemes(self):
      return {
          EmbedderScheme.Empty: [],
          EmbedderScheme.Shallow: [self.dense_layer(128)],
          # dqn
          EmbedderScheme.Medium: [self.dense_layer(256)],
          # carla
          EmbedderScheme.Deep: [
              self.dense_layer(128),
              self.dense_layer(128),
              self.dense_layer(128),
          ],
      }
  
file_path: coach/rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py
imports: from typing import List
  import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
  from rl_coach.base_parameters import EmbedderScheme
  from rl_coach.core_types import InputVectorEmbedding
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