code: def node_base_ctor_call(self, schema: LazyIrSchema) -> str:
      value_args = schema.filtered_args(values=True, scalars=False)
      # backends can customize the way the node base class constructor is called,
      # as long as all of its arguments can be generated from information available from the schema
      base_ctor_value_args_list = []
      for arg in value_args:
          if isinstance(arg.lazy_type, (BaseCType, VectorCType)):
              base_ctor_value_args_list.append(f"{arg.name}")
          elif isinstance(arg.lazy_type, OptionalCType):
              base_ctor_value_args_list.append(
                  f"{arg.name}.value_or(kNullValue)"
              )
          else:
              raise AssertionError(
                  f"Unsupported type ({arg.lazy_type}) - add support if necessary"
              )
      base_ctor_value_args = ", ".join(base_ctor_value_args_list)
  
      scalar_args = schema.filtered_args(values=False, scalars=True)
  
      # Shape construction.
      # Conditionally build shape depending on specified shape property
      if schema.properties.ShapePrecompute:
          shape_ctor_arg = "std::move(shapes),"
      elif schema.properties.ShapeCompute:
          shape_args = [a.name for a in value_args]
          shape_args.extend(a.name for a in scalar_args)
          shape_ctor_arg = (
              f"compute_shape_{schema.name}({', '.join(shape_args)}),"
          )
      elif schema.properties.ShapeCache:
          shape_args = [f"operand({i})" for i in range(len(value_args))]
          shape_args.extend(a.name for a in scalar_args)
          shape_ctor_arg = f"[&](){{ return compute_shape_{schema.name}({', '.join(shape_args)})[0]; }},"
      else:
          shape_ctor_arg = ""
  
      scalar_hashes = ", ".join(f"{a.name}" for a in scalar_args)
  
      return f"""{self.node_base}(
                {schema.node_name}::ClassOpKind(),
                OpList{{{base_ctor_value_args}}},
                {shape_ctor_arg}
                /* num_outputs */ {len(schema.returns)},
                torch::lazy::MHash({scalar_hashes}))"""
  
imports: import itertools
  from abc import ABC
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torchgen.api.dispatcher
  from torchgen.api.lazy import getValueT, isValueType, LazyArgument, LazyIrProperties, LazyIrSchema, tensorListValueT
  from torchgen.api.translate import translate
  from torchgen.api.types import BaseCType, Binding, deviceT, DispatcherSignature, kernel_signature, NativeSignature, OptionalCType, VectorCType
  from torchgen.context import method_with_native_function
  from torchgen.dest.lazy_ts_lowering import ts_lowering_body
  from torchgen.model import Argument, BackendIndex, BackendMetadata, BaseTy, BaseType, FunctionSchema, ListType, NativeFunction, NativeFunctionsGroup
label_desc: Use image node_id 5 for calling the GenLazyIR obj's underlying member method code with example usage: obj.node_base_ctor_call(schema) and returns: self, str, schema, str, base_ctor_value_args, str, shape_ctor_arg, str, len, str, scalar_hashes, str