code: def lstm_with_dot_attention_reference_different_dim(
      input,
      initial_hidden_state,
      initial_cell_state,
      initial_attention_weighted_encoder_context,
      gates_w,
      gates_b,
      decoder_input_lengths,
      weighted_decoder_hidden_state_t_w,
      weighted_decoder_hidden_state_t_b,
      encoder_outputs_transposed,
  ):
      return lstm_with_dot_attention_reference(
          input=input,
          initial_hidden_state=initial_hidden_state,
          initial_cell_state=initial_cell_state,
          initial_attention_weighted_encoder_context=(
              initial_attention_weighted_encoder_context
          ),
          gates_w=gates_w,
          gates_b=gates_b,
          decoder_input_lengths=decoder_input_lengths,
          encoder_outputs_transposed=encoder_outputs_transposed,
          weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
          weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
      )
  
imports: from caffe2.python import core, gradient_checker, rnn_cell, workspace, scope, utils
  from caffe2.python.attention import AttentionType
  from caffe2.python.model_helper import ModelHelper, ExtractPredictorNet
  from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
  from caffe2.proto import caffe2_pb2
  import caffe2.python.hypothesis_test_util
  from functools import partial
  from hypothesis import assume, given
  from hypothesis import settings
  import hypothesis.strategies
  import numpy
  import unittest
label_desc: Use image node_id 15 for calling a global function with example usage: lstm_with_dot_attention_reference_different_dim(input, initial_hidden_state, initial_cell_state, initial_attention_weighted_encoder_context, gates_w, gates_b, decoder_input_lengths, weighted_decoder_hidden_state_t_w, weighted_decoder_hidden_state_t_b, encoder_outputs_transposed) and returns: lstm_with_dot_attention_reference