code: def _run_forward_and_backward(self, model, inp, weights, state):
      def forward(inp, weights):
          return model.pure_fn(
              inp, weights, state, rng=jax.random.PRNGKey(0)
          )
  
      out, vjpfun, new_state = jax.vjp(
          forward, inp, weights, has_aux=True
      )
      inp_grad, weights_grad = vjpfun(fastmath.numpy.ones_like(inp))
      return out, new_state, inp_grad, weights_grad
  
imports: from absl.testing import parameterized
  import jax
  import numpy
  from tensorflow import test
  from trax import fastmath
  from trax import shapes
  from trax.fastmath import numpy
  from trax.layers.research import efficient_attention
label_desc: Use image node_id 5 for calling the EfficientAttentionTest obj's underlying member method code with example usage: obj._run_forward_and_backward(model, inp, weights, state) and returns: out,  new_state,  inp_grad,  weights_grad, model