class_docstr: Class for composing individual encoding stages.
  
  This class provides functionality for arbitrarily composing individual
  encoding stages (implementations of `EncodingStageInterface` or
  `AdaptiveEncodingStageInterface`) into a tree, where an output `Tensor` of an
  encoding stage can be further encoded by another encoding stage.
  
  The interface is similar to that of `AdaptiveEncodingStageInterface`, with the
  additional methods `decode_before_sum` and `decode_after_sum`. These methods
  split the decoding functionality to two parts, based on commutativity with
  summation.
  
  Similar to the `AdaptiveEncodingStageInterface`, the methods are designed to
  be functional transformations. That means, for instance, that the
  `initial_state` and `update_state` methods do not modify any underlying state.
  Rather, the user of the `Encoder` class is responsible for storing any values
  if necessary, and providing them back to appropriate methods of this class.
docstr: Gets parameters controlling the behavior of the Encoder.
  
  Args:
    state: A dictionary of the same structure as returned by the
      `initial_state` and `update_state` methods.
    name: `string`, name of the operation.
  
  Returns:
    A tuple `(encode_params, decode_params)`, where these are the parameters
    expected by the `encode` and `decode` methods, respectively. Both of them
    are dictionaries with two keys: `EncoderKeys.PARAMS` and
    `EncoderKeys.CHILDREN`. The `EncoderKeys.PARAMS` key maps to a dictionary
    containing the parameters of the encoding stage controlled by this class.
    The `EncoderKeys.CHILDREN` key maps to a dictionary with the same keys as
    `self.children`, each of which maps to an object like this one,
    recursively.
docstr_tok: ["Gets","parameters","controlling","the","behavior","of","the","Encoder",".","Args",":","state",":","A","dictionary","of","the","same","structure","as","returned","by","the","`","initial_state","`","and","`","update_state","`","methods",".","name",":","`","string","`",",","name","of","the","operation",".","Returns",":","A","tuple","`","(","encode_params",",","decode_params",")","`",",","where","these","are","the","parameters","expected","by","the","`","encode","`","and","`","decode","`","methods",",","respectively",".","Both","of","them","are","dictionaries","with","two","keys",":","`","EncoderKeys.PARAMS","`","and","`","EncoderKeys.CHILDREN","`",".","The","`","EncoderKeys.PARAMS","`","key","maps","to","a","dictionary","containing","the","parameters","of","the","encoding","stage","controlled","by","this","class",".","The","`","EncoderKeys.CHILDREN","`","key","maps","to","a","dictionary","with","the","same","keys","as","`","self.children","`",",","each","of","which","maps","to","an","object","like","this","one",",","recursively","."]
code: def get_params(self, state, name=None):
      """Gets parameters controlling the behavior of the Encoder.
  
      Args:
        state: A dictionary of the same structure as returned by the
          `initial_state` and `update_state` methods.
        name: `string`, name of the operation.
  
      Returns:
        A tuple `(encode_params, decode_params)`, where these are the parameters
        expected by the `encode` and `decode` methods, respectively. Both of them
        are dictionaries with two keys: `EncoderKeys.PARAMS` and
        `EncoderKeys.CHILDREN`. The `EncoderKeys.PARAMS` key maps to a dictionary
        containing the parameters of the encoding stage controlled by this class.
        The `EncoderKeys.CHILDREN` key maps to a dictionary with the same keys as
        `self.children`, each of which maps to an object like this one,
        recursively.
      """
      with tf.compat.v1.name_scope(
          name, "encoder_get_params", tf.nest.flatten(state)
      ):
          return self._get_params_impl(state)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.core import encoding_stage
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.utils import py_utils
label_desc: Use image node_id 10 for calling the Encoder obj's underlying member method code with example usage: obj.get_params(state, name) and returns: self