code: def process_ir_type(
      typ: Type, properties: "LazyIrProperties", *, symint: bool
  ) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]:
      """
      This function takes a type from NativeFunctions and converts it for use with
      lazy tensor codegen.
  
      Type conversion for lazy currently consists of
       (1) changing at::Tensors into lazy::Values
       (2) wrapping everything in a BaseCType
       (3) making cpp-reference types into cpp-value types (e.g. vector instead of IntArrayRef)
  
      (1) converts at::Tensors to lazy::Values (which wrap lazy::Nodes, with which Lazy IR represents tensors.)
      There is special handling for Optional[Tensor] or List[Tensor], etc- hence 'tensor-like'
  
      This is incomplete- there are assertions in places that it's expected to need to add
      more types as the codegen is used with more operators.
      """
      if isinstance(typ, BaseType):
          if typ.name == BaseTy.Tensor:
              return BaseCType(getValueT())
          elif typ.name == BaseTy.Scalar:
              if properties.TreatScalarsAsConstants:
                  return BaseCType(scalarT)
              # at::scalar has special handling,
              # and is wrapped in an lazy::Value just like at::tensor
              return BaseCType(getValueT())
          elif typ.name == BaseTy.ScalarType:
              return BaseCType(scalarTypeT)
          elif typ.name == BaseTy.int:
              return BaseCType(longT)
          elif typ.name == BaseTy.SymInt:
              if symint:
                  return BaseCType(getValueT())
              else:
                  return BaseCType(longT)
          elif typ.name == BaseTy.bool:
              return BaseCType(boolT)
          elif typ.name == BaseTy.float:
              return BaseCType(doubleT)
          elif typ.name == BaseTy.str:
              return BaseCType(stringT)
          elif typ.name == BaseTy.Device:
              return BaseCType(deviceT)
          elif typ.name == BaseTy.Generator:
              return BaseCType(generatorT)
          elif typ.name == BaseTy.Layout:
              return BaseCType(layoutT)
          elif typ.name == BaseTy.MemoryFormat:
              return BaseCType(memoryFormatT)
          else:
              raise AssertionError(
                  f"TODO add support for type {repr(typ)}"
              )
      elif isinstance(typ, OptionalType):
          return OptionalCType(
              process_ir_type(typ.elem, properties, symint=symint)
          )
      elif isinstance(typ, ListType):
          if str(typ.elem) == "Tensor?":
              # TODO(whc) is this actually correct? or should it use a Vector like above
              return ListCType(OptionalCType(BaseCType(getValueT())))
          elif str(typ.elem) == "Tensor":
              # this is a TensorList which comes in from GetTensorList as a Value
              return BaseCType(tensorListValueT)
          elif typ.elem == BaseType(BaseTy.SymInt):
              # TODO: return a value type.  The problem here is analogous to
              # the problem with tensorListValueT: if you have SymInt[] you
              # cannot conveniently save the list of Value directly, as nodes
              # expect to save values as a vector for ALL arguments.  So you
              # need a separate IR node that represents all of the size nodes
              # assembled into a list.  I'm not an LTC dev so I don't want to
              # figure it out right now.  Y'all figure it out...
              return VectorCType(BaseCType(longT))
  
          else:
              return VectorCType(
                  process_ir_type(typ.elem, properties, symint=symint)
              )
      else:
          raise AssertionError(f"unrecognized type {repr(typ)}")
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  from torchgen.api.types import BaseCppType, BaseCType, boolT, CType, deviceT, doubleT, generatorT, layoutT, ListCType, longT, memoryFormatT, NamedCType, OptionalCType, scalarT, scalarTypeT, stringT, SymIntT, VectorCType
  from torchgen.model import Argument, BaseTy, BaseType, FunctionSchema, ListType, OperatorName, OptionalType, Return, TensorOptionsArguments, Type
label_desc: Use image node_id 3 for calling a global function with example usage: process_ir_type(typ, properties) and returns: BaseCType, OptionalCType, BaseCType, BaseCType, BaseCType, ListCType, BaseCType, BaseCType, VectorCType, VectorCType, BaseCType, BaseCType, BaseCType, BaseCType, BaseCType, BaseCType, BaseCType, BaseCType, BaseCType