code: def call_function(
      self,
      tx,
      args: List[VariableTracker],
      kwargs: Dict[str, VariableTracker],
  ) -> VariableTracker:
      if len(args) == 1 and isinstance(args[0], TensorVariable):
          from .builder import VariableBuilder
          from .torch_function import TensorWithTFOverrideVariable
  
          torch_fn = VariableBuilder(
              tx, AttrSource(self.source, "__torch_function__")
          )(self.value.__torch_function__)
  
          return TensorWithTFOverrideVariable.from_tensor_var(
              tx, args[0], self.value, torch_fn
          )
  
      return super().call_function(tx, args, kwargs)
  
imports: import functools
  import inspect
  import operator
  import types
  from typing import Dict, List
  import sympy
  import torch._numpy
  import torch.fx
  import torch.random
  from torch._dynamo import compiled_autograd
  from torch.fx.experimental.symbolic_shapes import guard_scalar, GuardOnDataDependentSymNode, has_free_symbols, is_symbolic, SymTypes
  from ..None import config, variables
  from .._trace_wrapped_higher_order_op import trace_wrapped
  from ..exc import unimplemented, UserError, UserErrorType
  from ..guards import GuardBuilder, install_guard
  from ..source import AttrSource
  from ..utils import fqn, get_custom_getattr, get_fake_value, get_real_value, guard_if_dyn, object_has_getattribute, product, proxy_args_kwargs, tensortype_to_dtype
  from .base import VariableTracker
  from .constant import ConstantVariable
  from .lists import SizeVariable
label_desc: Use image node_id 2 for calling the TensorSubclassVariable obj's underlying member method code with example usage: obj.call_function(tx, args, kwargs) and returns: super, TensorWithTFOverrideVariable