code: def cond_branch_nested_function(x):
      """
      The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:
        - both branches must take the same args, which must also match the branch args passed to cond.
        - both branches must return a single tensor
        - returned tensor must have the same tensor metadata, e.g. shape and dtype
        - branch function can be free function, nested function, lambda, class methods
        - branch function can not have closure variables
        - no inplace mutations on inputs or global variables
  
      This example demonstrates using nested function in cond().
  
      NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
      """
  
      def true_fn(x):
          def inner_true_fn(y):
              return x + y
  
          return inner_true_fn(x)
  
      def false_fn(x):
          def inner_false_fn(y):
              return x - y
  
          return inner_false_fn(x)
  
      return cond(x.shape[0] < 10, true_fn, false_fn, [x])
  
label_desc: Use image node_id 1 for calling a global function with example usage: cond_branch_nested_function(x) and returns: cond, inner_true_fn, inner_false_fn, unknown, unknown