code: def _forward_and_or_backward(layer):
      """Create forward_and_or_backward for layers that don't define it."""
  
      def forward_and_or_backward(
          inputs,
          weights,
          state,
          rng,
          output_grad=None,
          compute_output=True,
          update_state=True,
      ):
          """Performs batched forward and/or backward passes.
  
          Args:
            inputs: inputs to the attention layer
            weights: weights for the attention layer
            state: state of the attention layer
            rng: PRNG key for the layer (shared across all examples and heads)
            output_grad: gradient of the loss wrt the output of the layer, or None.
                This function performs the backward pass iff `output_grad` is not
                None.
            compute_output: bool: whether to return the output of the forward pass
                (for example, a pure backwards pass does not need to return the
                output).
            update_state: bool: whether to return an updated layer state.
  
          Returns:
            A tuple (output, new_state, inputs_grad, weights_grad).
            - output is not None iff compute_output is True
            - new_state is not None iff update_state is True
            - inputs_grad & weights_grad are not None iff output_grad is not None
          """
          # Calculate the vector-Jacobian product of the layer pure_fn.
          output, vjp_fn, new_state = fastmath.vjp(
              layer.pure_fn, inputs, weights, state, rng, has_aux=True
          )
          output = output if compute_output else None
          new_state = new_state if update_state else None
  
          # The vjp function returns gradients with respect to inputs and weights.
          if output_grad is not None:
              grads_inputs, grads_weights, _, _ = vjp_fn(output_grad)
          else:
              grads_inputs, grads_weights = None, None
  
          return (output, new_state, grads_inputs, grads_weights)
  
      return forward_and_or_backward
  
imports: from absl import logging
  import jax
  from trax import fastmath
  from trax.layers import base
  from trax.layers import combinators
label_desc: Use image node_id 2 for calling a global function with example usage: _forward_and_or_backward(layer) and returns: forward_and_or_backward, output,  new_state,  grads_inputs,  grads_weights