code: def test_atomic_convolution():
      """Test invoking AtomicConvolution."""
      batch_size = 4
      max_atoms = 5
      max_neighbors = 2
      dimensions = 3
      params = [[5.0, 2.0, 0.5], [10.0, 2.0, 0.5]]
      input1 = np.random.rand(batch_size, max_atoms, dimensions).astype(
          np.float32
      )
      input2 = np.random.randint(
          max_atoms, size=(batch_size, max_atoms, max_neighbors)
      )
      input3 = np.random.randint(
          1, 10, size=(batch_size, max_atoms, max_neighbors)
      )
      layer = layers.AtomicConvolution(radial_params=params)
      result = layer([input1, input2, input3])
      assert result.shape == (batch_size, max_atoms, len(params))
      assert len(layer.trainable_variables) == 3
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 19 for calling a global function with example usage: test_atomic_convolution() without return types