code: def _validate_outputs(self, tfssa, outputs):
      if outputs is None:
          return
      outputs = (
          outputs if isinstance(outputs, (tuple, list)) else [outputs]
      )
      output_nodes = []
      for f in tfssa.functions.values():
          output_nodes += list(f.outputs)
      all_nodes = []
      for f in tfssa.functions.values():
          all_nodes += list(f.graph.keys())
      for n in outputs:
          if self._get_tensor_name(n) not in output_nodes + all_nodes:
              raise KeyError(
                  'Output node name "{}" does exist.'.format(n)
              )
  
imports: from coremltools import _logger
  from coremltools.converters._profile_utils import _profile
  from coremltools.converters.mil._deployment_compatibility import AvailableTarget
  from coremltools.converters.mil.input_types import ImageType, InputType, RangeDim
  from coremltools.converters.mil.input_types import Shape
  from coremltools.converters.mil.input_types import TensorType, _get_shaping_class
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program, get_new_symbol, types
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic
  from coremltools.converters.mil.mil.var import Var
  from .._utils import get_output_names
  from .basic_graph_ops import simple_topsort
  from .convert_utils import convert_graph
label_desc: Use image node_id 5 for calling the TFConverter obj's underlying member method code with example usage: obj._validate_outputs(tfssa, outputs) without return types