code: def tensor_attr_supported_getter(func, *args, **kwargs):
      if func == torch.ops.aten.is_non_overlapping_and_dense.default:
          return False
  
      if func == torch.ops.aten.sym_size.default:
          return args[0]._size
  
      if func == torch.ops.aten.dim.default:
          return len(args[0]._size)
  
      if func == torch.ops.aten.sym_numel.default:
          if args[0]._lengths is not None:
              return int(
                  sum(args[0]._lengths) * math.prod(args[0]._size[2:])
              )
          return args[0]._values.numel()
  
      if func == torch.ops.aten.sym_stride.default:
          return args[0]._strides
  
      if func == torch.ops.aten.sym_storage_offset.default:
          return args[0]._values.storage_offset()
  
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 14 for calling a global function with example usage: tensor_attr_supported_getter(func) and returns: False, args, len, args, args, args, int