code: def _apply(
      self, model, input_t, seq_lengths, states, timestep, extra_inputs
  ):
      assert len(states) == 1
      result = model.net.Mul([input_t, states[0]])
      model.net.AddExternalOutput(result)
      return [result]
  
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 1 for calling the MulCell obj's underlying member method code with example usage: obj._apply(model, input_t, seq_lengths, states, timestep, extra_inputs) and returns: list