class_docstr: Builds a traditional GRU cell with dense internal transformations.
  
  Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
class_docstr_tok: ["Builds","a","traditional","GRU","cell","with","dense","internal","transformations",".","Gated","Recurrent","Unit","paper",":","https",":","\/\/arxiv.org\/abs\/1412.3555"]
code: def __init__(
      self,
      n_units,
      forget_bias=0.0,
      kernel_initializer=initializers.RandomUniformInitializer(0.01),
      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 GRUCell object from inherited base classes: base with example: obj = GRUCell(n_units, forget_bias, kernel_initializer, bias_initializer)