code: def GeneralGRUCell(
      candidate_transform,
      memory_transform_fn=None,
      gate_nonlinearity=activation_fns.Sigmoid,
      candidate_nonlinearity=activation_fns.Tanh,
      dropout_rate_c=0.1,
      sigmoid_bias=0.5,
  ):
      r"""Parametrized Gated Recurrent Unit (GRU) cell construction.
  
    GRU update equations for update gate, reset gate, candidate memory, and new
    state:
  
    .. math::
      u_t &= \sigma(U' \times s_{t-1} + B') \\
      r_t &= \sigma(U'' \times s_{t-1} + B'') \\
      c_t &= \tanh(U \times (r_t \odot s_{t-1}) + B) \\
      s_t &= u_t \odot s_{t-1} + (1 - u_t) \odot c_t
  
    See `combinators.Gate` for details on the gating function.
  
  
    Args:
      candidate_transform: Transform to apply inside the Candidate branch. Applied
        before nonlinearities.
      memory_transform_fn: Optional transformation on the memory before gating.
      gate_nonlinearity: Function to use as gate activation; allows trying
        alternatives to `Sigmoid`, such as `HardSigmoid`.
      candidate_nonlinearity: Nonlinearity to apply after candidate branch; allows
        trying alternatives to traditional `Tanh`, such as `HardTanh`.
      dropout_rate_c: Amount of dropout on the transform (c) gate. Dropout works
        best in a GRU when applied exclusively to this branch.
      sigmoid_bias: Constant to add before sigmoid gates. Generally want to start
        off with a positive bias.
  
    Returns:
      A model representing a GRU cell with specified transforms.
    """
      gate_block = [  # u_t
          candidate_transform(),
          _AddSigmoidBias(sigmoid_bias),
          gate_nonlinearity(),
      ]
      reset_block = [  # r_t
          candidate_transform(),
          _AddSigmoidBias(sigmoid_bias),  # Want bias to start positive.
          gate_nonlinearity(),
      ]
      candidate_block = [
          cb.Dup(),
          reset_block,
          cb.Multiply(),  # Gate S{t-1} with sigmoid(candidate_transform(S{t-1}))
          candidate_transform(),  # Final projection + tanh to get Ct
          candidate_nonlinearity(),  # Candidate gate
          # Only apply dropout on the C gate. Paper reports 0.1 as a good default.
          core.Dropout(rate=dropout_rate_c),
      ]
      memory_transform = (
          memory_transform_fn() if memory_transform_fn else []
      )
      return cb.Serial(
          cb.Branch(memory_transform, gate_block, candidate_block),
          cb.Gate(),
      )
  
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 5 for calling a global function with example usage: GeneralGRUCell(candidate_transform, memory_transform_fn, gate_nonlinearity, candidate_nonlinearity, dropout_rate_c, sigmoid_bias) and returns: cb