class_docstr: TF GRU Cell with extra customization params. 
class_docstr_tok: ["TF","GRU","Cell","with","extra","customization","params","."]
docstr: Gated recurrent unit (GRU) with nunits cells.
code: def __call__(self, inputs, state, scope=None):
      """Gated recurrent unit (GRU) with nunits cells."""
      with tf.variable_scope(scope or type(self).__name__):  # "GRUCell"
          with tf.variable_scope(
              "Gates"
          ):  # Reset gate and update gate.
              # We start with bias of 1.0 to not reset and not update.
              _w = _linear(
                  [inputs, state],
                  2 * self._num_units,
                  True,
                  1.0,
                  self.weights_init,
                  self.trainable,
                  self.restore,
                  self.reuse,
              )
              r, u = array_ops.split(
                  value=_w, num_or_size_splits=2, axis=1
              )
              r, u = self._inner_activation(r), self._inner_activation(
                  u
              )
          with tf.variable_scope("Candidate"):
              c = self._activation(
                  _linear(
                      [inputs, r * state],
                      self._num_units,
                      True,
                      0.0,
                      self.weights_init,
                      self.trainable,
                      self.restore,
                      self.reuse,
                  )
              )
          new_h = u * state + (1 - u) * c
  
          self.W, self.b = list(), list()
          # Retrieve RNN Variables
          with tf.variable_scope("Gates/Linear", reuse=True):
              self.W.append(tf.get_variable("Matrix"))
              self.b.append(tf.get_variable("Bias"))
          with tf.variable_scope("Candidate/Linear", reuse=True):
              self.W.append(tf.get_variable("Matrix"))
              self.b.append(tf.get_variable("Bias"))
  
      return new_h, new_h
  
imports: from __future__ import division, print_function, absolute_import
  import logging
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.ops import array_ops
  from tensorflow.python.util.nest import is_sequence
  from ..None import config
  from ..None import utils
  from ..None import activations
  from ..None import initializations
  from ..None import variables
  from .normalization import batch_normalization
label_desc: Use image node_id 4 for calling the GRUCell obj's underlying member method code with example usage: obj.__call__(inputs, state, scope) and returns: new_h,  new_h