class_docstr: An input embedder is the first part of the network, which takes the input from the state and produces a vector
  embedding by passing it through a neural network. The embedder will mostly be input type dependent, and there
  can be multiple embedders in a single network
class_docstr_tok: ["An","input","embedder","is","the","first","part","of","the","network",",","which","takes","the","input","from","the","state","and","produces","a","vector","embedding","by","passing","it","through","a","neural","network",".","The","embedder","will","mostly","be","input","type","dependent",",","and","there","can","be","multiple","embedders","in","a","single","network"]
code: def __str__(self):
      result = ["Input size = {}".format(self._input_size)]
      if self.input_rescaling != 1.0 or self.input_offset != 0.0:
          result.append(
              "Input Normalization (scale = {}, offset = {})".format(
                  self.input_rescaling, self.input_offset
              )
          )
      result.extend([str(l) for l in self.layers_params])
      if not self.layers_params:
          result.append("No layers")
  
      return "\n".join(result)
  
file_path: coach/rl_coach/architectures/tensorflow_components/embedders/embedder.py
imports: from typing import List, Union, Tuple
  import copy
  import numpy
  import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense, Flatten
  from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
  from rl_coach.core_types import InputEmbedding
  from rl_coach.utils import force_list
label_desc: Use image node_id 8 for calling the InputEmbedder obj's underlying member method code with example usage: obj.__str__() and returns: str