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 input_size(self, value: Union[int, List[int]]):
      if isinstance(value, np.ndarray) or isinstance(value, tuple):
          value = list(value)
      elif isinstance(value, int):
          value = [value]
      if not isinstance(value, list):
          raise ValueError(
              (
                  "input_size expected to be a list, found {value} which has type {type}"
              ).format(value=value, type=type(value))
          )
      self._input_size = value
  
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 5 for calling the InputEmbedder obj's underlying member method code with example usage: obj.input_size(value) without return types