code: def test_computation_padding_same(self, with_bias):
      expected_out = np.asarray(
          [
              8,
              12,
              8,
              12,
              18,
              12,
              8,
              12,
              8,
              12,
              18,
              12,
              18,
              27,
              18,
              12,
              18,
              12,
              8,
              12,
              8,
              12,
              18,
              12,
              8,
              12,
              8,
          ]
      ).reshape((3, 3, 3))
      if with_bias:
          expected_out += 1
  
      def f():
          data = jnp.ones([1, 3, 3, 3, 1])
          net = conv.Conv3DTranspose(
              output_channels=1,
              kernel_shape=3,
              stride=1,
              padding="SAME",
              with_bias=with_bias,
              **create_constant_initializers(1.0, 1.0, with_bias),
          )
          return net(data)
  
      init_fn, apply_fn = transform.transform(f)
      out = apply_fn(init_fn(random.PRNGKey(428)), None)
  
      self.assertEqual(out.shape, (1, 3, 3, 3, 1))
      out = np.squeeze(out, axis=(0, 4))
      np.testing.assert_allclose(out, expected_out, rtol=1e-5)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from haiku._src import conv
  from haiku._src import initializers
  from haiku._src import test_utils
  from haiku._src import transform
  import jax
  from jax import random
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 for calling the Conv3DTransposeTest obj's underlying member method code with example usage: obj.test_computation_padding_same(with_bias) and returns: net