docstr: Test 2D convolution with circular padding: square filter with all elements
  equal to 1 applied on an input with all elements equal to 1.
  Result should have the same shape as input (except for the feature
  dimension) and have all elements equal to
  `n_input_features * kernel_lin_size^2`.
docstr_tok: ["Test","2D","convolution","with","circular","padding",":","square","filter","with","all","elements","equal","to","1","applied","on","an","input","with","all","elements","equal","to","1",".","Result","should","have","the","same","shape","as","input","(","except","for","the","feature","dimension",")","and","have","all","elements","equal","to","`","n_input_features","*","kernel_lin_size^2","`","."]
code: def test_circular_conv_2d_constant(
      self,
      n_batch,
      n_features,
      kernel_lin_size,
      n_input_features,
      input_x_size,
      input_y_size,
      module,
  ):
      """
      Test 2D convolution with circular padding: square filter with all elements
      equal to 1 applied on an input with all elements equal to 1.
      Result should have the same shape as input (except for the feature
      dimension) and have all elements equal to
      `n_input_features * kernel_lin_size^2`.
      """
      rng = dict(params=random.key(0))
      x = jnp.ones(
          (n_batch, input_x_size, input_y_size, n_input_features)
      )
      kernel_size = (kernel_lin_size, kernel_lin_size)
      conv_module = module(
          features=n_features,
          kernel_size=kernel_size,
          padding="CIRCULAR",
          kernel_init=initializers.ones,
          bias_init=initializers.zeros,
      )
      y, initial_params = conv_module.init_with_output(rng, x)
  
      kernel_shape = self._get_kernel_shape(
          x.shape, kernel_size, module, n_features
      )
  
      self.assertEqual(
          initial_params["params"]["kernel"].shape,
          kernel_shape,
      )
      correct_ans = np.full(
          (n_batch, input_x_size, input_y_size, n_features),
          kernel_lin_size * kernel_lin_size * n_input_features,
      )
      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 21 for calling the LinearTest obj's underlying member method code with example usage: obj.test_circular_conv_2d_constant(n_batch, n_features, kernel_lin_size, n_input_features, input_x_size, input_y_size, module) without return types