docstr: An vector embedder is an input embedder that takes an vector input from the state and produces a vector
  embedding by passing it through a neural network.
  
  :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function
      and dropout properties.
docstr_tok: ["An","vector","embedder","is","an","input","embedder","that","takes","an","vector","input","from","the","state","and","produces","a","vector","embedding","by","passing","it","through","a","neural","network",".",":","param","params",":","parameters","object","containing","input_clipping",",","input_rescaling",",","batchnorm",",","activation_function","and","dropout","properties","."]
code: def __init__(self, params: InputEmbedderParameters):
      """
      An vector embedder is an input embedder that takes an vector input from the state and produces a vector
      embedding by passing it through a neural network.
  
      :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function
          and dropout properties.
      """
      super(VectorEmbedder, self).__init__(params)
      self.input_rescaling = params.input_rescaling["vector"]
      self.input_offset = params.input_offset["vector"]
  
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 1 to create a new VectorEmbedder object from inherited base classes: InputEmbedder with example: obj = VectorEmbedder(params)