code: def testComputationPaddingSame(self, with_bias):
      expected_out = np.asarray(
          [
              9,
              13,
              13,
              13,
              9,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              9,
              13,
              13,
              13,
              9,
              13,
              19,
              19,
              19,
              13,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              19,
              28,
              28,
              28,
              19,
              13,
              19,
              19,
              19,
              13,
              9,
              13,
              13,
              13,
              9,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              13,
              19,
              19,
              19,
              13,
              9,
              13,
              13,
              13,
              9,
          ]
      ).reshape((5, 5, 5))
      if not with_bias:
          expected_out -= 1
  
      conv1 = conv.Conv3D(
          output_channels=1,
          kernel_shape=3,
          stride=1,
          padding="SAME",
          with_bias=with_bias,
          **create_constant_initializers(1.0, 1.0, with_bias),
      )
  
      out = conv1(tf.ones([1, 5, 5, 5, 1], dtype=tf.float32))
      self.assertEqual(out.shape, [1, 5, 5, 5, 1])
      out = tf.squeeze(out, axis=(0, 4))
  
      self.assertAllClose(self.evaluate(out), expected_out)
  
imports: from absl.testing import parameterized
  import numpy
  from sonnet.src import conv
  from sonnet.src import initializers
  from sonnet.src import test_utils
  import tensorflow
label_desc: Use image node_id 1 for calling the Conv3DTest obj's underlying member method code with example usage: obj.testComputationPaddingSame(with_bias) without return types