code: def test_custom_zero_grad(self, backend):
      class IdWithZeroGrad(tl.Layer):
          def forward(self, x):
              return x
  
          @property
          def has_backward(self):
              return True
  
          def backward(
              self, inputs, output, grad, weights, state, new_state, rng
          ):
              return (jnp.zeros_like(grad), ())
  
      with fastmath.use_backend(backend):
          layer = IdWithZeroGrad()
          rng = fastmath.random.get_prng(0)
          input_signature = shapes.ShapeDtype((9, 17))
          random_input = fastmath.random.uniform(
              rng, input_signature.shape, minval=-1.0, maxval=1.0
          )
          layer.init(input_signature)
          f = lambda x: jnp.mean(layer(x))
          grad = fastmath.grad(f)(random_input)
          self.assertEqual(
              grad.shape, (9, 17)
          )  # Gradient for each input.
          self.assertEqual(sum(sum(grad * grad)), 0.0)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from trax import fastmath
  from trax import shapes
  from trax.fastmath import numpy
  import trax.layers
label_desc: Use image node_id 6 for calling the BaseLayerTest obj's underlying member method code with example usage: obj.test_custom_zero_grad(backend) and returns: x, True, jnp,  