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 __init__(
      self,
      input_size: List[int],
      activation_function=tf.nn.relu,
      scheme: EmbedderScheme = EmbedderScheme.Medium,
      batchnorm: bool = False,
      dropout_rate: float = 0.0,
      name: str = "embedder",
      input_rescaling: float = 1.0,
      input_offset: float = 0.0,
      input_clipping=None,
      dense_layer=Dense,
      is_training=False,
      flatten=True,
  ):
      super().__init__(
          input_size,
          activation_function,
          scheme,
          batchnorm,
          dropout_rate,
          name,
          input_rescaling,
          input_offset,
          input_clipping,
          dense_layer=dense_layer,
          is_training=is_training,
          flatten=flatten,
      )
  
      self.return_type = InputVectorEmbedding
      if len(self.input_size) != 1 and scheme != EmbedderScheme.Empty:
          raise ValueError(
              "The input size of a vector embedder must contain only a single dimension"
          )
  
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 1 to create a new VectorEmbedder object from inherited base classes: InputEmbedder with example: obj = VectorEmbedder(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling, input_offset, input_clipping, dense_layer, is_training, flatten)