code: def test_DAG_layer():
      """Test invoking DAGLayer."""
      batch_size = 10
      n_graph_feat = 30
      n_atom_feat = 75
      max_atoms = 50
      layer_sizes = [100]
      atom_features = np.random.rand(batch_size, n_atom_feat)
      parents = np.random.randint(
          0, max_atoms, size=(batch_size, max_atoms, max_atoms)
      )
      calculation_orders = np.random.randint(
          0, batch_size, size=(batch_size, max_atoms)
      )
      calculation_masks = np.random.randint(
          0, 2, size=(batch_size, max_atoms)
      )
      # Recall that the DAG layer expects a MultiConvMol as input,
      # so the "batch" is a pooled set of atoms from all the
      # molecules in the batch, just as it is for the graph conv.
      # This means that n_atoms is the batch-size
      n_atoms = batch_size
      # dropout_switch = False
      layer = layers.DAGLayer(
          n_graph_feat=n_graph_feat,
          n_atom_feat=n_atom_feat,
          max_atoms=max_atoms,
          layer_sizes=layer_sizes,
      )
      outputs = layer(
          [  # noqa: F841
              atom_features,
              parents,
              calculation_orders,
              calculation_masks,
              n_atoms,
              # dropout_switch
          ]
      )
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 26 for calling a global function with example usage: test_DAG_layer() without return types