class_docstr: TF GRU Cell with extra customization params. 
class_docstr_tok: ["TF","GRU","Cell","with","extra","customization","params","."]
code: def __init__(
      self,
      num_units,
      input_size=None,
      activation=tf.tanh,
      inner_activation=tf.sigmoid,
      bias=True,
      weights_init=None,
      trainable=True,
      restore=True,
      reuse=False,
  ):
      if input_size is not None:
          logging.warning(
              "%s: The input_size parameter is deprecated." % self
          )
      self._num_units = num_units
      if isinstance(activation, str):
          self._activation = activations.get(activation)
      elif hasattr(activation, "__call__"):
          self._activation = activation
      else:
          raise ValueError("Invalid Activation.")
      if isinstance(inner_activation, str):
          self._inner_activation = activations.get(inner_activation)
      elif hasattr(inner_activation, "__call__"):
          self._inner_activation = inner_activation
      else:
          raise ValueError("Invalid Activation.")
      self.bias = bias
      self.weights_init = weights_init
      if isinstance(weights_init, str):
          self.weights_init = initializations.get(weights_init)()
      self.trainable = trainable
      self.restore = restore
      self.reuse = reuse
  
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 1 to create a new GRUCell object from inherited base classes: core_rnn_cell with example: obj = GRUCell(num_units, input_size, activation, inner_activation, bias, weights_init, trainable, restore, reuse)