code: def get_supported_param_types():
      data = [
          # (python type, schema type, type[] variant, type?[] variant, type[]? variant
          (torch.Tensor, "Tensor", True, True, False),
          (int, "SymInt", True, False, True),
          (float, "float", True, False, True),
          (bool, "bool", True, False, True),
          (str, "str", False, False, False),
          (torch.types.Number, "Scalar", True, False, False),
          (torch.dtype, "ScalarType", False, False, False),
          (torch.device, "Device", False, False, False),
      ]
      result = []
      for line in data:
          result.extend(derived_types(*line))
      return dict(result)
  
imports: import dataclasses
  import functools
  import inspect
  import sys
  import typing
  import weakref
  from torchgen.model import FunctionSchema, OperatorName, SchemaKind, BaseType, ListType, BaseTy
  import torch
  import torch._C
  import torch.library
  from torch._library.abstract_impl import AbstractImplCtx
  from torch.library import get_ctx
  from .autograd import autograd_kernel_indirection, construct_autograd_kernel
label_desc: Use image node_id 12 for calling a global function with example usage: get_supported_param_types() and returns: dict