docstr: Test 1D transposed convolution with circular padding: 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`.
docstr_tok: ["Test","1D","transposed","convolution","with","circular","padding",":","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","`","."]
code: def test_circular_conv_transpose_1d_constant(
      self,
      n_batch,
      n_features,
      kernel_size,
      n_input_features,
      input_size,
  ):
      """
      Test 1D transposed convolution with circular padding: 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`.
      """
      rng = dict(params=random.key(0))
      x = jnp.ones((n_batch, input_size, n_input_features))
      conv_module = nn.ConvTranspose(
          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)
  
      self.assertEqual(
          initial_params["params"]["kernel"].shape,
          (kernel_size, n_input_features, n_features),
      )
      correct_ans = np.full(
          (n_batch, input_size, n_features),
          kernel_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 33 for calling the LinearTest obj's underlying member method code with example usage: obj.test_circular_conv_transpose_1d_constant(n_batch, n_features, kernel_size, n_input_features, input_size) without return types