code: def test_atomic_conv_model_reload():
      from deepchem.models.atomic_conv import AtomicConvModel
      from deepchem.data import NumpyDataset
  
      model_dir = tempfile.mkdtemp()
      batch_size = 1
      N_atoms = 5
  
      acm = AtomicConvModel(
          n_tasks=1,
          batch_size=batch_size,
          layer_sizes=[
              1,
          ],
          frag1_num_atoms=5,
          frag2_num_atoms=5,
          complex_num_atoms=10,
          model_dir=model_dir,
      )
  
      features = []
      frag1_coords = np.random.rand(N_atoms, 3)
      frag1_nbr_list = {0: [], 1: [], 2: [], 3: [], 4: []}
      frag1_z = np.random.randint(10, size=(N_atoms))
      frag2_coords = np.random.rand(N_atoms, 3)
      frag2_nbr_list = {0: [], 1: [], 2: [], 3: [], 4: []}
      frag2_z = np.random.randint(10, size=(N_atoms))
      system_coords = np.random.rand(2 * N_atoms, 3)
      system_nbr_list = {
          0: [],
          1: [],
          2: [],
          3: [],
          4: [],
          5: [],
          6: [],
          7: [],
          8: [],
          9: [],
      }
      system_z = np.random.randint(10, size=(2 * N_atoms))
  
      features.append(
          (
              frag1_coords,
              frag1_nbr_list,
              frag1_z,
              frag2_coords,
              frag2_nbr_list,
              frag2_z,
              system_coords,
              system_nbr_list,
              system_z,
          )
      )
      features = np.asarray(features)
      labels = np.random.rand(batch_size)
      dataset = NumpyDataset(features, labels)
  
      acm.fit(dataset, nb_epoch=1)
  
      reloaded_model = AtomicConvModel(
          n_tasks=1,
          batch_size=batch_size,
          layer_sizes=[
              1,
          ],
          frag1_num_atoms=5,
          frag2_num_atoms=5,
          complex_num_atoms=10,
          model_dir=model_dir,
      )
      reloaded_model.restore()
  
      # Check predictions match on random sample
      origpred = acm.predict(dataset)
      reloadpred = reloaded_model.predict(dataset)
      assert np.all(origpred == reloadpred)
  
imports: import os
  import pytest
  import tempfile
  import numpy
  import deepchem
  import scipy.io
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS
  from deepchem.feat import create_char_to_idx
label_desc: Use image node_id 6 for calling a global function with example usage: test_atomic_conv_model_reload() without return types