code: def test_snli(self):
      class Encoder(nn.Module):
          def __init__(self, config):
              super(Encoder, self).__init__()
              # self.config = config
              input_size = (
                  config["d_proj"]
                  if config["projection"]
                  else config["d_embed"]
              )
              dropout = (
                  0 if config["n_layers"] == 1 else config["dp_ratio"]
              )
              self.rnn = nn.LSTM(
                  input_size=input_size,
                  hidden_size=config["d_hidden"],
                  num_layers=config["n_layers"],
                  dropout=dropout,
                  bidirectional=config["birnn"],
              )
              self.n_cells = config["n_cells"]
              self.d_hidden = config["d_hidden"]
              self.birnn = config["birnn"]
  
          def forward(self, inputs):
              batch_size = inputs.size()[1]
              state_shape = self.n_cells, batch_size, self.d_hidden
              h0 = c0 = inputs.new_zeros(state_shape)
              outputs, (ht, ct) = self.rnn(inputs, (h0, c0))
              return (
                  ht[-1]
                  if not self.birnn
                  else ht[-2:]
                  .transpose(0, 1)
                  .contiguous()
                  .view(batch_size, -1)
              )
  
      class SNLIClassifier(nn.Module):
          def __init__(self, config):
              super(SNLIClassifier, self).__init__()
              self.embed = nn.Embedding(
                  config["n_embed"], config["d_embed"]
              )
              self.projection = Linear(
                  config["d_embed"], config["d_proj"]
              )
              self.encoder = Encoder(config)
              self.dropout = nn.Dropout(p=config["dp_ratio"])
              self.relu = nn.ReLU()
              seq_in_size = 2 * config["d_hidden"]
              if config["birnn"]:
                  seq_in_size *= 2
              lin_config = [seq_in_size] * 2
              self.out = nn.Sequential(
                  Linear(*lin_config),
                  self.relu,
                  self.dropout,
                  Linear(*lin_config),
                  self.relu,
                  self.dropout,
                  Linear(*lin_config),
                  self.relu,
                  self.dropout,
                  Linear(seq_in_size, config["d_out"]),
              )
              self.fix_emb = config["fix_emb"]
              self.project = config["projection"]
  
          def forward(self, premise, hypothesis):
              prem_embed = self.embed(premise)
              hypo_embed = self.embed(hypothesis)
              if self.fix_emb:
                  prem_embed = prem_embed.detach()
                  hypo_embed = hypo_embed.detach()
              if self.project:
                  prem_embed = self.relu(self.projection(prem_embed))
                  hypo_embed = self.relu(self.projection(hypo_embed))
              premise = self.encoder(prem_embed)
              hypothesis = self.encoder(hypo_embed)
              scores = self.out(torch.cat([premise, hypothesis], 1))
              return scores
  
      Config = {
          "n_embed": 100,
          "d_embed": 100,
          "d_proj": 300,
          "dp_ratio": 0.0,  # For deterministic testing TOD": change by fixing seed in checkTrace?,
          "d_hidden": 30,
          "birnn": True,
          "d_out": 300,
          "fix_emb": True,
          "projection": True,
          "n_layers": 2,
          "n_cells": 4,  # 2 * n_layers because birnn = True,
      }
  
      premise = torch.LongTensor(48, 64).random_(0, 100)
      hypothesis = torch.LongTensor(24, 64).random_(0, 100)
  
      self.checkExportImport(
          SNLIClassifier(Config), (premise, hypothesis)
      )
  
imports: import unittest
  import torch
  import torch.nn.functional
  import torchvision
  import nni.nas.nn.pytorch.layers
  from nni.nas.nn.pytorch import ParametrizedModule
  from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
label_desc: Use image node_id 5 for calling the TestConvert obj's underlying member method code with example usage: obj.test_snli() and returns: unknown, scores