code: def _make_rnn_direction(
      cls,
      input_blob,
      B,
      W,
      R,
      initial_states_and_names,
      sequence_lens,
      pred_mh,
      init_net,
      input_size,
      hidden_size,
      num_gates,
      direction_offset,
      Bi,
      Br,
      W_,
      R_,
      reform,
      make_cell,
      keep_outputs,
  ):
      name = cls.dummy_name()
  
      # input and recurrence biases are squashed together in onnx
      # but not in caffe2
      gates_hidden_size = num_gates * hidden_size
      bias_offset = 2 * direction_offset * gates_hidden_size
      weight_offset = direction_offset * gates_hidden_size
      Bi = init_net.Slice(
          B,
          name + Bi,
          starts=[bias_offset + 0 * gates_hidden_size],
          ends=[bias_offset + 1 * gates_hidden_size],
      )
      Br = init_net.Slice(
          B,
          name + Br,
          starts=[bias_offset + 1 * gates_hidden_size],
          ends=[bias_offset + 2 * gates_hidden_size],
      )
      W_ = init_net.Slice(
          W,
          name + W_,
          starts=[weight_offset + 0 * gates_hidden_size, 0],
          ends=[weight_offset + 1 * gates_hidden_size, -1],
      )
      R_ = init_net.Slice(
          R,
          name + R_,
          starts=[weight_offset + 0 * gates_hidden_size, 0],
          ends=[weight_offset + 1 * gates_hidden_size, -1],
      )
  
      initial_states_sliced = []
      for initial_state, name_suffix in initial_states_and_names:
          initial_states_sliced.append(
              pred_mh.net.Slice(
                  initial_state,
                  name + name_suffix,
                  starts=[direction_offset + 0, 0, 0],
                  ends=[direction_offset + 1, -1, -1],
              )
          )
  
      if direction_offset == 1:
          if sequence_lens is not None:
              seq_lens_for_reverse = sequence_lens
          else:
              input_shape = pred_mh.net.Shape(
                  input_blob, name + "/input_shape"
              )
              batch_size = pred_mh.net.Slice(
                  input_shape,
                  name + "/batch_size_slice",
                  starts=[1],
                  ends=[2],
              )
              seq_len = pred_mh.net.Slice(
                  input_shape,
                  name + "/seq_len_slice",
                  starts=[0],
                  ends=[1],
              )
              dummy_sequence_lens = pred_mh.net.Tile(
                  [seq_len, batch_size],
                  name + "/dummy_sequence_lens",
                  axis=0,
              )
              pred_mh.net.Reshape(
                  dummy_sequence_lens,
                  [dummy_sequence_lens, cls.dummy_name()],
                  shape=[-1],
              )
              seq_lens_for_reverse = pred_mh.net.Cast(
                  dummy_sequence_lens,
                  name + "/seq_lens_for_reverse",
                  to=core.DataType.INT32,
              )
      reform(Bi, Br, W_, R_, name, hidden_size, init_net)
  
      if direction_offset == 1:
          input = pred_mh.net.ReversePackedSegs(
              [input_blob, seq_lens_for_reverse],
              name + "/input-reversed",
          )
      else:
          input = input_blob
  
      outputs = keep_outputs(
          list(
              make_cell(
                  pred_mh,
                  input,
                  sequence_lens,
                  initial_states_sliced,
                  input_size,
                  hidden_size,
                  name,
                  drop_states=False,
                  forward_only=True,
              )
          )
      )
  
      if direction_offset == 1:
          outputs[0] = pred_mh.net.ReversePackedSegs(
              [outputs[0], seq_lens_for_reverse],
              name + "/output-reversed",
          )
  
      return outputs
  
imports: import collections
  import sys
  import zipfile
  import itertools
  import onnx.backend
  from caffe2.python import core, workspace, rnn_cell, gru_cell
  from caffe2.python.model_helper import ModelHelper
  from caffe2.proto import caffe2_pb2
  import caffe2.python.utils
  import numpy
  import onnx
  from onnx import TensorProto
  import onnx.numpy_helper
  import onnx.defs
  import onnx.shape_inference
  import onnx.utils
  from onnx.backend.base import Backend, Device, DeviceType, namedtupledict
  from caffe2.python.onnx.workspace import Workspace
  from caffe2.python.onnx.backend_rep import Caffe2Rep
  import caffe2.python._import_c_extension
  import warnings
label_desc: Use image node_id 5 for calling the Caffe2Backend obj's underlying member method code with example usage: obj._make_rnn_direction(cls, input_blob, B, W, R, initial_states_and_names, sequence_lens, pred_mh, init_net, input_size, hidden_size, num_gates, direction_offset, Bi, Br, W_, R_, reform, make_cell, keep_outputs) and returns: outputs