docstr: functional: input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean',
  label_smoothing=0.0
  
  non functional ctor: weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean',
  label_smoothing=0.0
  
  non functional loss call: input, target, optional_output
docstr_tok: ["functional",":","input",",","target",",","weight=None",",","size_average=None",",","ignore_index=-","100",",","reduce=None",",","reduction='mean","'",",","label_smoothing=0.0","non","functional","ctor",":","weight=None",",","size_average=None",",","ignore_index=-","100",",","reduce=None",",","reduction='mean","'",",","label_smoothing=0.0","non","functional","loss","call",":","input",",","target",",","optional_output"]
code: def _call_cross_entropy_loss(self, tx, args, kwargs):
      """
      functional: input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean',
      label_smoothing=0.0
  
      non functional ctor: weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean',
      label_smoothing=0.0
  
      non functional loss call: input, target, optional_output
      """
      from . import ConstantVariable
  
      def normalize_args(
          weight=ConstantVariable.create(None),
          size_average=ConstantVariable.create(None),
          ignore_index=ConstantVariable.create(-100),
          reduce=ConstantVariable.create(None),
          reduction=ConstantVariable.create("mean"),
          label_smoothing=ConstantVariable.create(0.0),
      ):
          return (
              weight,
              size_average,
              ignore_index,
              reduce,
              reduction,
              label_smoothing,
          )
  
      (
          weight,
          size_average,
          ignore_index,
          reduce_arg,
          reduction,
          label_smoothing,
      ) = normalize_args(*args, **kwargs)
  
      def fake_cross_entropy_loss(input, target):
          from .builder import wrap_fx_proxy
  
          return wrap_fx_proxy(
              tx=tx,
              proxy=tx.output.create_proxy(
                  "call_function",
                  torch.nn.functional.cross_entropy,
                  *proxy_args_kwargs(
                      [
                          input,
                          target,
                          weight,
                          size_average,
                          ignore_index,
                          reduce_arg,
                          reduction,
                          label_smoothing,
                      ],
                      {},
                  ),
              ),
          )
  
      return variables.LambdaVariable(fake_cross_entropy_loss)
  
imports: import collections
  import contextlib
  import functools
  import importlib
  import inspect
  import itertools
  import random
  import sys
  import threading
  import types
  from typing import Dict, List
  import torch._dynamo.config
  import torch.nn
  from torch._guards import TracingContext
  from ..None import variables
  from ..exc import unimplemented
  from ..guards import GuardBuilder, install_guard
  from ..source import AttrSource, GetItemSource, ODictGetItemSource, RandomValueSource
  from ..utils import all_hook_names, build_checkpoint_variable, check_constant_args, get_custom_getattr, has_torch_function, is_namedtuple_cls, is_utils_checkpoint, istype, namedtuple_fields, object_has_getattribute, proxy_args_kwargs, tensortype_to_dtype
  from .base import MutableLocal, VariableTracker
  from .ctx_manager import GenericContextWrappingVariable, NullContextVariable
  from .dicts import DefaultDictVariable
label_desc: Use image node_id 10 for calling the UserDefinedClassVariable obj's underlying member method code with example usage: obj._call_cross_entropy_loss(tx, args, kwargs) and returns: variables, weight,  size_average,  ignore_index,  reduce,  reduction,  label_smoothing, wrap_fx_proxy