// The name of the XLA device (e.g., "XLA_CPU")
    string device_name;

    // The number of the device.
    int device_ordinal = -1;

    // The name of the compilation device (e.g., "XLA_CPU_JIT");
    string compilation_device_name;

    // If 'use_multiple_streams' is true, we create separate streams for
    // compute, host-to-device, and device-to-host communication.
    bool use_multiple_streams = false;

  absl::StatusOr<XlaCompiler::CompilationResult> compile_result =
      LegalizeMlirToHlo(function_to_hlo_args, metadata_proto, use_tuple_args,
                        /*device_type=*/"XLA_CPU_JIT",
                        custom_legalization_passes,
                        /*shape_determination_fns=*/{}, arg_shapes,
                        &arg_core_mapping, &per_core_arg_shapes, client);

// RUN: tf-opt "-xla-legalize-tf=device-type=XLA_CPU_JIT prefer-tf2xla=true use-tf2xla-fallback=true" %s -verify-diagnostics -mlir-disable-threading  | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  // CHECK-LABEL: binary_op
  func.func @binary_op(%arg0: tensor<2xf32>, %arg1: tensor<2xf32>) -> tensor<2xf32> {
    // CHECK: mhlo.atan2

      "Convert TF dialect to HLO dialect (used for compilation in bridge).",
      [](mlir::OpPassManager& pm) {
        tensorflow::CreateConvertMlirToXlaHloPipeline(
            pm, /*device_type=*/"XLA_CPU_JIT", /*enable_op_fallback=*/false,
            /*custom_legalization_passes=*/{});
      });
}

  target.addIllegalOp<TF::XlaConvV2Op>();
  target.addIllegalOp<TF::XlaGatherOp>();

  RewritePatternSet patterns(context);
  mhlo::Tf2XlaTypeConverter converter;
  mhlo::PopulateLegalizeTfWithTf2XlaPatterns("XLA_CPU_JIT", patterns, context,
                                             converter);
  mhlo::PopulateLegalizeTfPatterns(context, &patterns);
  mlir::odml::PopulateLegalizeHloToTfPatterns(&patterns, context);