code: def test_multibatch_input_conv_transpose(self, use_bias):
      rng = dict(params=random.key(0))
      x = jnp.ones((2, 5, 8, 3))
      conv_transpose_module = nn.ConvTranspose(
          features=4,
          use_bias=use_bias,
          kernel_size=(3,),
          padding="VALID",
          kernel_init=initializers.ones,
          bias_init=initializers.ones,
      )
      y, initial_params = conv_transpose_module.init_with_output(rng, x)
      self.assertEqual(
          initial_params["params"]["kernel"].shape, (3, 3, 4)
      )
      correct_ans = np.array(
          [
              [
                  [4.0, 4.0, 4.0, 4.0],
                  [7.0, 7.0, 7.0, 7.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [10.0, 10.0, 10.0, 10.0],
                  [7.0, 7.0, 7.0, 7.0],
                  [4.0, 4.0, 4.0, 4.0],
              ]
          ]
      )
      correct_ans = np.repeat(correct_ans[None], repeats=2, axis=0)
      correct_ans = np.repeat(correct_ans, repeats=5, axis=1)
      if not use_bias:
          correct_ans -= 1.0
      np.testing.assert_allclose(y, correct_ans)
  
imports: import functools
  import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest, parameterized
  from jax import random
  from jax.nn import initializers
  from flax import linen
label_desc: Use image node_id 30 for calling the LinearTest obj's underlying member method code with example usage: obj.test_multibatch_input_conv_transpose(use_bias) without return types