SmallVector<int64_t, 8> perm0_values;
  for (const auto &value : perm0.getValues<APInt>())
    perm0_values.push_back(value.getSExtValue());

  SmallVector<int64_t, 8> perm1_values;
  for (const auto &value : perm1.getValues<APInt>())
    perm1_values.push_back(value.getSExtValue());

  for (int i = 0; i < perm0_values.size(); ++i) {
    if (perm0_values[perm1_values[i]] != i) return false;
  }

  const std::string gpu1 = "/job:worker/replica:1/task:2/device:GPU:1";

  llvm::SmallVector<std::unique_ptr<Device>, 2> devices;
  devices.push_back(FakeDevice::Make(cpu0));
  devices.push_back(FakeDevice::Make(gpu0, "compute capability: 7.0"));
  devices.push_back(FakeDevice::Make(gpu1));

  DeviceSet device_set;
  for (auto& device : devices) device_set.AddDevice(device.get());
  AddDevicesToOp(*module_ref, &device_set);

    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);
    input_tys.push_back(mlir_ty);
  }
  for (auto ty : output_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));
    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);
    output_tys.push_back(mlir_ty);
  }
  llvm::SmallVector<mlir::NamedAttribute, 4> attrs;

  // collection, so read from metagraph first.
  if (meta_graph_def.asset_file_def_size() > 0) {
    for (const auto& asset : meta_graph_def.asset_file_def()) {
      asset_file_defs->push_back(asset);
    }
    return absl::OkStatus();
  }
  // Fall back to read from collection to be backward compatible with v1.
  const auto& collection_def_map = meta_graph_def.collection_def();

      if (!func_regex.match(func.getName())) {
        continue;
      }

      ApplyFunctionTacFilter(func, tac_filter.function_filter().filter_type(),
                             builder);
      filtered_ops.push_back(func);
    }
    return;
  }

  llvm::Regex op_regex(tac_filter.op_filter().op_name_pattern());
  module.walk([&](Operation* op) {
    auto named_loc = mlir::dyn_cast<NameLoc>(op->getLoc());

  // Read all resource variables from the session.
  std::vector<std::string> variable_names;
  variable_names.reserve(resource_names.size());
  for (StringRef name : resource_names) variable_names.push_back(name.str());

  std::vector<Tensor> resource_tensors;
  Status status = session->Run(
      /*inputs=*/{}, variable_names,
      /*target_tensor_names=*/{}, &resource_tensors);
  if (!status.ok()) {

  builder.setInsertionPointAfter(*old_launch_op);

  llvm::SmallVector<Type, 4> new_launch_op_results_types;
  for (Value result : new_launch_op_results)
    new_launch_op_results_types.push_back(result.getType());

  auto new_launch_op = builder.create<tf_device::LaunchOp>(
      old_launch_op->getLoc(), old_launch_op->getDeviceAttr(),
      /*result_types=*/new_launch_op_results_types);

                            device_info_cache.GetIdFor(device_name));
    device_set.Insert(device_id);
    expected_devices.push_back(device_name);
  }

  device_set.ForEach([&](jit::DeviceId device_id) {
    actual_devices.push_back(string(device_info_cache.GetNameFor(device_id)));
    return true;
  });

  EXPECT_EQ(expected_devices, actual_devices);
}

    cluster_func_ops.push_back(cluster_func);
  });
  EXPECT_EQ(cluster_func_ops.size(), 1);

  EXPECT_TRUE(mlir::succeeded(tensorflow::EraseClusterFuncs(cluster_func_ops)));

  llvm::SmallVector<mlir::tf_device::ClusterFuncOp, 4> new_cluster_func_ops;
  module->walk([&](mlir::tf_device::ClusterFuncOp cluster_func) {
    new_cluster_func_ops.push_back(cluster_func);
  });

    VLOG(1) << "Not creating XLA devices, tf_xla_enable_xla_devices not set "
               "and XLA device creation not requested";
    return absl::OkStatus();
  }

  devices->push_back(absl::StrCat("/physical_device:", DEVICE_XLA_CPU, ":0"));
  return absl::OkStatus();
}

Status XlaCpuDeviceFactory::CreateDevices(
    const SessionOptions& session_options, const string& name_prefix,