import_config.enable_shape_inference = false;
  import_config.xla_compile_device_type =
      function_options.xla_compile_device_type;
  import_config.enable_soft_placement = function_options.allow_soft_placement;

  static const char* kTfMlirCategory = "TfMlir";
  tensorflow::metrics::ScopedCounter<2> timings(
      tensorflow::metrics::GetGraphOptimizationCounter(),
      {kTfMlirCategory, "convert_graph_to_mlir"});

unified attributes. For example, `_tpu_replicate="cluster"` in the
following code

```mlir

// CHECK-NEXT: return

// CHECK-LABEL: func private @func2
func.func private @func2(%arg: tensor<i1>) -> tensor<i32> {
  // CHECK-NOT: tf._TfrtGetResource

  // CHECK-NEXT:   "tf.NoOp"
  // CHECK-NEXT:   tf_device.return

  AddRewrittenEmbeddingOps(module.getContext(), &supported_ops);
  AddRewrittenCompositeOps(module.getContext(), &supported_ops);

  auto result = module.walk([&](mlir::tf_device::ClusterOp cluster) {
    // Only if `allow_soft_placement` attribute is true should we mark ops
    // for outside compilation.
    auto soft_placement_attr =
        cluster->getAttrOfType<BoolAttr>(mlir::TF::kAllowSoftPlacementAttr);

    unified attributes. For example, `_tpu_replicate="cluster"` in the
    following code

    ```mlir

      tf_device.return %1 : tensor<?x!tf_type.string>
    }, {

      }, {
        "tf.Yield"() : () -> ()
      }) {is_stateless = false} : (tensor<i1>) -> ()
      tf_device.return

    // expected-remark@above {{ID: 2}}
    tf_device.return %2#0, %2#1 : tensor<i32>, tensor<i32>
    // expected-remark@above {{ID: 3}}

      attrs.push_back(
          b.getNamedAttr("_xla_compile_device_type",
                         b.getStringAttr(specs.xla_compile_device_type)));
    }
    attrs.push_back(b.getNamedAttr("allow_soft_placement",
                                   b.getBoolAttr(specs.enable_soft_placement)));
  } else {
    // Collects the argument and return nodes by looking up the node names
    // specified by the user.