code: def test_neural_fingerprint_retrieval():
      tasks, dataset, _, _ = get_dataset("classification", "GraphConv")
  
      fp_size = 3
  
      batch_size = 50
      model = GraphConvModel(
          len(tasks),
          batch_size=batch_size,
          dense_layer_size=3,
          mode="classification",
      )
  
      model.fit(dataset, nb_epoch=1)
      neural_fingerprints = model.predict_embedding(dataset)
      neural_fingerprints = np.array(neural_fingerprints)[
          : len(dataset)
      ]
      assert (len(dataset), fp_size * 2) == neural_fingerprints.shape
  
imports: import os
  import numpy
  import pytest
  from scipy import io
  from deepchem.data import NumpyDataset, CSVLoader
  from deepchem.trans import DAGTransformer
  from deepchem.molnet import load_bace_classification, load_delaney
  from deepchem.feat import ConvMolFeaturizer
  from deepchem.metrics import Metric, roc_auc_score, mean_absolute_error
  from deepchem.utils.data_utils import download_url, get_data_dir
  from flaky import flaky
label_desc: Use image node_id 3 for calling a global function with example usage: test_neural_fingerprint_retrieval() without return types