class_docstr: TF Basic LSTM recurrent network cell with extra customization params.
  
  The implementation is based on: http://arxiv.org/abs/1409.2329.
  
  We add forget_bias (default: 1) to the biases of the forget gate in order to
  reduce the scale of forgetting in the beginning of the training.
  
  It does not allow cell clipping, a projection layer, and does not
  use peep-hole connections: it is the basic baseline.
  
  For advanced models, please use the full LSTMCell that follows.
class_docstr_tok: ["TF","Basic","LSTM","recurrent","network","cell","with","extra","customization","params",".","The","implementation","is","based","on",":","http",":","\/\/arxiv.org\/abs\/1409.2329",".","We","add","forget_bias","(","default",":","1",")","to","the","biases","of","the","forget","gate","in","order","to","reduce","the","scale","of","forgetting","in","the","beginning","of","the","training",".","It","does","not","allow","cell","clipping",",","a","projection","layer",",","and","does","not","use","peep-hole","connections",":","it","is","the","basic","baseline",".","For","advanced","models",",","please","use","the","full","LSTMCell","that","follows","."]
code: def __init__(
      self,
      num_units,
      forget_bias=1.0,
      input_size=None,
      state_is_tuple=True,
      activation=tf.tanh,
      inner_activation=tf.sigmoid,
      bias=True,
      weights_init=None,
      trainable=True,
      restore=True,
      reuse=False,
      batch_norm=False,
  ):
      if not state_is_tuple:
          logging.warning(
              "%s: Using a concatenated state is slower and will soon be "
              "deprecated.  Use state_is_tuple=True." % self
          )
      if input_size is not None:
          logging.warning(
              "%s: The input_size parameter is deprecated." % self
          )
      self._num_units = num_units
      self._forget_bias = forget_bias
      self._state_is_tuple = state_is_tuple
      self.batch_norm = batch_norm
      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 BasicLSTMCell object from inherited base classes: core_rnn_cell with example: obj = BasicLSTMCell(num_units, forget_bias, input_size, state_is_tuple, activation, inner_activation, bias, weights_init, trainable, restore, reuse, batch_norm)