docstr: Test 1d convolution with circular padding and kernel dilation.
docstr_tok: ["Test","1d","convolution","with","circular","padding","and","kernel","dilation","."]
code: def test_circular_conv_1d_dilation(self):
      """Test 1d convolution with circular padding and kernel dilation."""
      rng = dict(params=random.key(0))
      x = np.arange(1, 6)
      x = np.expand_dims(x, (0, 2))
      kernel = np.array((1, 2, 1))
      kernel = np.expand_dims(kernel, (1, 2))
  
      conv_module = nn.Conv(
          features=1,
          kernel_size=(3,),
          padding="CIRCULAR",
          kernel_init=lambda *_: kernel,
          bias_init=initializers.zeros,
          kernel_dilation=(3,),
      )
      y, initial_params = conv_module.init_with_output(rng, x)
  
      self.assertEqual(
          initial_params["params"]["kernel"].shape, (3, 1, 1)
      )
      # Compare with manually computed convolution
      correct_ans = np.array(
          (
              3 + 2 * 1 + 4,
              4 + 2 * 2 + 5,
              5 + 2 * 3 + 1,
              1 + 2 * 4 + 2,
              2 + 2 * 5 + 3,
          )
      )
      correct_ans = np.expand_dims(correct_ans, (0, 2))
      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 24 for calling the LinearTest obj's underlying member method code with example usage: obj.test_circular_conv_1d_dilation() without return types