class_docstr: Class that handles conversion of pytorch models to the MIL format.
  
  Models passed to the @TorchConverter go from:
  Loaded-Torch Model -> Internal Graph -> PyMIL
docstr: Returns the set of ops in @graph that are implemented, and the set
  for which no conversion function is registered. @graph can be
  either InternalTorchIRGraph or InternalTorchIRBlock.
docstr_tok: ["Returns","the","set","of","ops","in","@","graph","that","are","implemented",",","and","the","set","for","which","no","conversion","function","is","registered",".","@","graph","can","be","either","InternalTorchIRGraph","or","InternalTorchIRBlock","."]
code: def _check_ops(graph):
      """
      Returns the set of ops in @graph that are implemented, and the set
      for which no conversion function is registered. @graph can be
      either InternalTorchIRGraph or InternalTorchIRBlock.
      """
      implemented_ops = set()
      missing_ops = set()
      for node in graph.nodes:
          _add_op = _TORCH_OPS_REGISTRY.get_func(node.kind)
          if _add_op is None:
              missing_ops.add(node.kind)
          else:
              implemented_ops.add(node.kind)
          for block in node.blocks:
              _impl, _miss = TorchConverter._check_ops(block)
              implemented_ops.update(_impl)
              missing_ops.update(_miss)
      return implemented_ops, missing_ops
  
imports: from collections import OrderedDict
  from typing import List, Optional, Union
  import numpy
  import torch
  from torch.jit._script import RecursiveScriptModule
  from coremltools import _logger
  from coremltools._deps import _HAS_TORCH_EXPORT_API
  from coremltools.converters.mil._deployment_compatibility import AvailableTarget
  from coremltools.converters.mil.input_types import ImageType, InputType, TensorType
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Placeholder, Program, types
  from coremltools.converters.mil.mil.types import is_float
  from coremltools.converters.mil.mil.var import Var
  from .._utils import get_output_names
  from .internal_graph import InternalTorchIRGraph, InternalTorchIRNode
  from .ops import TorchFrontend, convert_nodes
  from .quantization_ops import _dequantized_weight
  from .torch_op_registry import _TORCH_OPS_REGISTRY
  from .torchir_passes import flatten_graph_input_values, flatten_graph_output_values, generate_tensor_assignment_ops, remove_getattr_nodes, transform_inplace_ops
  from .torchscript_utils import torch_to_mil_types
label_desc: Use image node_id 4 for calling the TorchConverter obj's underlying member method code with example usage: obj._check_ops(graph) and returns: implemented_ops,  missing_ops