code: def custom_grad(*args, **kwargs):
      """Set a custom gradient computation (override the default) for a function."""
      return backend()["custom_grad"](*args, **kwargs)
  
imports: import contextlib
  import enum
  import gin
  from trax.fastmath.jax import JAX_BACKEND
  from trax.fastmath.numpy import NUMPY_BACKEND
  from trax.fastmath.tf import TF_BACKEND
label_desc: Use image node_id 32 for calling a global function with example usage: custom_grad() and returns: backend