code: def test_dense_general_vs_numpy(self, axis, batch_dims, einsum_expr):
      rng = dict(params=random.key(0))
      x = jnp.ones((16, 8, 9, 10))
  
      dg_module = nn.DenseGeneral(
          features=(11, 12),
          axis=axis,
          batch_dims=batch_dims,
          bias_init=initializers.ones,
          kernel_init=initializers.normal(),
      )
      y, initial_params = dg_module.init_with_output(rng, x)
      target = (
          np.einsum(einsum_expr, x, initial_params["params"]["kernel"])
          + 1.0
      )
      np.testing.assert_allclose(y, target, atol=1e-6)
  
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 9 for calling the LinearTest obj's underlying member method code with example usage: obj.test_dense_general_vs_numpy(axis, batch_dims, einsum_expr) without return types