code: def lookup_jagged(func, *args, **kwargs) -> Optional[Callable]:
      dispatch_func = JAGGED_OPS_TABLE.get(func, None)
      if dispatch_func is not None:
          return dispatch_func
  
      # Handle pointwise fallbacks
      if torch.Tag.pointwise in func.tags:
          # Assume there aren't additional tensors that aren't the "unary/binary" args
          num_tensor_args = sum(
              [isinstance(x, torch.Tensor) for x in args]
          )
          if num_tensor_args == 1:
              check_schema("self: jt, ...", func, *args, **kwargs)
              return functools.partial(jagged_unary_pointwise, func)
          elif num_tensor_args == 2:
              check_schema("lhs: any, rhs: any", func, *args, **kwargs)
              return functools.partial(jagged_binary_pointwise, func)
  
      return None
  
imports: import functools
  import math
  import operator
  import torch
  from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention
  from .nested_tensor import NestedTensor
  from typing import *
  from torch.fx.operator_schemas import normalize_function
label_desc: Use image node_id 9 for calling a global function with example usage: lookup_jagged(func) and returns: None, dispatch_func, functools, functools