@__reduce_func(%arg0: tensor<i32> {tf._user_specified_name = "args_0"}, %arg1: tensor<i32> {tf._user_specified_name = "args_1"}, %arg2: tensor<i32> {tf._user_specified_name = "args_2"}, %arg3: tensor<i32> {tf._user_specified_name = "args_3"}, %arg4: tensor<?x!tf_type.string> {tf._user_specified_name = "args_4"}, %arg5: tensor<?x!tf_type.string> {tf._user_specified_name = "args_5"}, %arg6: tensor<?x!tf_type.string> {tf._user_specified_name = "args_6"}, %arg7: tensor<?x?x512xf32> {tf._user_specified_name = "args_7"},...

// Start step 0
C_0 = cond(args_0)
N_0 = non_tpu(args_0)
if (C_0) {
   F_0 = forward(args_0, N_0)
   T_0 = core_tpu(args_0, N_0, F_0)
   // B_0 = backward() is not evaluated here.
}

args_1 = update_args(args_0, N_0, T_0)

// Start step 1
C_1 = cond(args_1)
N_1 = non_tpu(args_1)
if (C_1) {
   F_1 = forward(args_1, N_1)
   // T_1 = core_tpu() is not evaluated here.

        assertThat(args2.length, is(1));

        final ParameterizedClassDesc arg2_1 = args2[0];
        assertThat(arg2_1.getRawClass(), is(sameClass(Integer.class)));
    }

    /**
     * @throws Exception
     */
    @Test
    public void testMethodParameterType() throws Exception {

// Simple case, single device cluster.

module {
  // CHECK-LABEL: func @singlecluster
  // CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<?xi32>)
  func.func @singlecluster(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    // CHECK: %[[A_OUTPUT:[0-9]*]] = "tf.A"(%[[ARG_0]])
    %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

    // CHECK: %[[TPU0_OUTPUT:[0-9]*]] = "tf_device.launch"

  EXPECT_THAT(index_path, NotNull());
  EXPECT_THAT(index_path, Eq("arg_0:0"));
}

TEST_F(ConvertAssetArgsTest, NonBoundedArgsNotModified) {
  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module {
      func.func @main(%arg_0: tensor<!tf_type.string> {tf_saved_model.index_path = ["arg_0:0"]}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }
    }
  )mlir");

Frank Wessels <******@****.***> 1705552466 -0800

  }

  // CHECK-LABEL: func private @main_0
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xbf16>, %[[ARG_1:.*]]: tensor<10xbf16>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func private @main_0(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) attributes {_from_xla_call_module} {
    // CHECK: %[[ADD:.*]] = stablehlo.add %[[ARG_0]], %[[ARG_1]] : tensor<10xbf16>

}

// -----

// CHECK-LABEL: func @merge_consecutive_qcast
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>) -> (tensor<?xf32>, tensor<?xf32>)
func.func @merge_consecutive_qcast(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2: tensor<?xf32>) -> (tensor<?xf32>, tensor<?xf32>) {
  // CHECK: "quantfork.qcast"(%[[ARG_1]])
  // CHECK-SAME: -> tensor<?x!quant.uniform<i8:f32, 0.02454993117089365:-64>>

// Tests simple case of a single `tf_device.cluster`.

// CHECK-LABEL: func @single_cluster
// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<?xi32>)
func.func @single_cluster(%arg0: tensor<?xi32>) -> tensor<?xi32> {
  %0 = tf_executor.graph {
    %1:2 = tf_executor.island {
      // CHECK: %[[A_OUTPUT:[0-9]*]] = "tf.A"(%[[ARG_0]])
      %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

     %arg60, %arg61, %arg62, %arg63, %arg64, %arg65, %arg66, %arg67, %arg68, %arg69, %arg70, %arg71, %arg72, %arg73, %arg74,
     %arg75, %arg76, %arg77, %arg78, %arg79, %arg80, %arg81, %arg82, %arg83, %arg84, %arg85, %arg86, %arg87, %arg88, %arg89,
     %arg90, %arg91, %arg92, %arg93, %arg94, %arg95, %arg96, %arg97, %arg98, %arg99, %arg100, %arg101, %arg102, %arg103, %arg104,