code: def __init__(
      self,
      num_units,
      use_peepholes=False,
      cell_clip=None,
      initializer=None,
      num_proj=None,
      proj_clip=None,
      num_unit_shards=None,
      num_proj_shards=None,
      forget_bias=1.0,
      state_is_tuple=True,
      activation=None,
      reuse=None,
  ):
      super(Time4ALSTMCell, self).__init__(_reuse=reuse)
      if not state_is_tuple:
          logging.warn(
              "%s: Using a concatenated state is slower and will soon be "
              "deprecated.  Use state_is_tuple=True.",
              self,
          )
      if num_unit_shards is not None or num_proj_shards is not None:
          logging.warn(
              "%s: The num_unit_shards and proj_unit_shards parameters are "
              "deprecated and will be removed in Jan 2017.  "
              "Use a variable scope with a partitioner instead.",
              self,
          )
  
      self._num_units = num_units
      self._use_peepholes = use_peepholes
      self._cell_clip = cell_clip
      self._initializer = initializer
      self._num_proj = num_proj
      self._proj_clip = proj_clip
      self._num_unit_shards = num_unit_shards
      self._num_proj_shards = num_proj_shards
      self._forget_bias = forget_bias
      self._state_is_tuple = state_is_tuple
      self._activation = activation or math_ops.tanh
  
      if num_proj:
          self._state_size = (
              LSTMStateTuple(num_units, num_proj)
              if state_is_tuple
              else num_units + num_proj
          )
          self._output_size = num_proj
      else:
          self._state_size = (
              LSTMStateTuple(num_units, num_units)
              if state_is_tuple
              else 2 * num_units
          )
          self._output_size = num_units
      self._linear1 = None
      self._linear2 = None
      self._time_input_w1 = None
      self._time_input_w2 = None
      self._time_kernel_w1 = None
      self._time_kernel_t1 = None
      self._time_bias1 = None
      self._time_kernel_w2 = None
      self._time_kernel_t2 = None
      self._time_bias2 = None
      self._o_kernel_t1 = None
      self._o_kernel_t2 = None
      if self._use_peepholes:
          self._w_f_diag = None
          self._w_i_diag = None
          self._w_o_diag = None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import partitioned_variables
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import nest
  from tensorflow.python.ops.rnn_cell_impl import RNNCell, LSTMStateTuple, _BIAS_VARIABLE_NAME, _WEIGHTS_VARIABLE_NAME
label_desc: Use image node_id 1 to create a new Time4ALSTMCell object from inherited base classes: RNNCell with example: obj = Time4ALSTMCell(num_units, use_peepholes, cell_clip, initializer, num_proj, proj_clip, num_unit_shards, num_proj_shards, forget_bias, state_is_tuple, activation, reuse)