class_docstr: LSTM Cell.
  
  For a nice overview of the motivation and (i, o, f) gates, see this tutorial:
  https://colah.github.io/posts/2015-08-Understanding-LSTMs/
  
  See this paper for a description and detailed study of all gate types:
  https://arxiv.org/pdf/1503.04069.pdf
class_docstr_tok: ["LSTM","Cell",".","For","a","nice","overview","of","the","motivation","and","(","i",",","o",",","f",")","gates",",","see","this","tutorial",":","https",":","\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/","See","this","paper","for","a","description","and","detailed","study","of","all","gate","types",":","https",":","\/\/arxiv.org\/pdf\/1503.04069.pdf"]
code: def __init__(
      self,
      n_units,
      forget_bias=1.0,
      kernel_initializer=initializers.GlorotUniformInitializer(),
      bias_initializer=initializers.RandomNormalInitializer(1e-6),
  ):
      super().__init__(n_in=2, n_out=2)
      self._n_units = n_units
      self._forget_bias = forget_bias
      self._kernel_initializer = kernel_initializer
      self._bias_initializer = bias_initializer
  
imports: from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import activation_fns
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
label_desc: Use image node_id 1 to create a new LSTMCell object from inherited base classes: base with example: obj = LSTMCell(n_units, forget_bias, kernel_initializer, bias_initializer)