code: def init_lists():
      path_to_script = pathlib.Path(
          os.path.abspath(os.path.dirname(__file__))
      )
      TS_NATIVE_FUNCTIONS_PATH = (
          path_to_script.parent.parent
          / "aten/src/ATen/native/ts_native_functions.yaml"
      )
      with open(TS_NATIVE_FUNCTIONS_PATH) as f:
          yaml_ts = yaml.load(f, yaml.SafeLoader)
      LAZY_OPS_LIST = set(
          remove_suffixes(
              itertools.chain(
                  yaml_ts["full_codegen"],
                  yaml_ts["supported"],
                  yaml_ts["autograd"],
              )
          )
      )
      HAS_SYMINT_SUFFIX = yaml_ts["symint"]
      FALLBACK_LIST = {"clamp"}
      SKIP_RUNTIME_ERROR_LIST = {
          "index_select",  # Empty output_sizes is not supported
          "clone",  # is clone decomposed?
          # General ASAN Failure due to related to generating bool values.
          # https://github.com/pytorch/pytorch/issues/74519
          # https://github.com/pytorch/pytorch/issues/63034
          "nonzero",  # ASAN failure (paste: P501906539)
          "all",  # ASAN failure
          "any",  # ASAN failure
          "logdet",  # ASAN failure
      }
      SKIP_INCORRECT_RESULTS_LIST = {
          "squeeze",  # Value out of range
          "t",  # Value out of range
          "transpose",  # Value out of range
          "bernoulli",  # incorrect results
          "pow",  # incorrect results
          "addcdiv",  # incorrect results (on CI not locally?)
      }
      # The following ops all show up directly in ts_native_functions.yaml,
      # but run functionalized versions of the composite kernels in core.
      # This means that we don't expect the ops to show directly in the LTC metrics.
      FUNCTIONAL_DECOMPOSE_LIST = {
          "diag_embed",
          "block_diag",
          "new_empty_strided",
          "narrow_copy",
          "pixel_shuffle",
          "pixel_unshuffle",
          "select_backward",
          "_trilinear",
          "linalg_inv_ex",
          "linalg_pinv.atol_rtol_tensor",
          "logsumexp",
      }
      # For some ops, we don't support all variants. Here we use formatted_name
      # to uniquely identify the variant.
      SKIP_VARIANT_LIST = {"norm_nuc", "min_reduction_with_dim"}
  
      return (
          LAZY_OPS_LIST,
          FALLBACK_LIST,
          SKIP_RUNTIME_ERROR_LIST,
          SKIP_INCORRECT_RESULTS_LIST,
          FUNCTIONAL_DECOMPOSE_LIST,
          HAS_SYMINT_SUFFIX,
          SKIP_VARIANT_LIST,
      )
  
imports: from typing import Sequence
  import torch
  import functools
  from torch.testing._internal.common_utils import run_tests, TestCase
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.common_methods_invocations import op_db
  from torch.testing._internal.common_device_type import ops, instantiate_device_type_tests
  import torch._lazy
  import torch._lazy.config
  import torch._lazy.metrics
  import torch._lazy.ir_cache
  import torch._lazy.ts_backend
  import itertools
  import yaml
  import os
  import pathlib
  from unittest import skip
label_desc: Use image node_id 3 for calling a global function with example usage: init_lists() and returns: LAZY_OPS_LIST,  FALLBACK_LIST,  SKIP_RUNTIME_ERROR_LIST,  SKIP_INCORRECT_RESULTS_LIST,  FUNCTIONAL_DECOMPOSE_LIST,  HAS_SYMINT_SUFFIX,  SKIP_VARIANT_LIST