E	SAME		x		(0,0)(0,0)
E	SAME		aaaaaa		(0,6)(0,6)
E	SAME		aaaaaax		(0,6)(0,6)
E	([a]*)+		a		(0,1)(0,1)
E	SAME		x		(0,0)(0,0)
E	SAME		aaaaaa		(0,6)(0,6)
E	SAME		aaaaaax		(0,6)(0,6)
E	([^b]*)*	a		(0,1)(0,1)
E	SAME		b		(0,0)(0,0)
E	SAME		aaaaaa		(0,6)(0,6)
E	SAME		aaaaaab		(0,6)(0,6)
E	([ab]*)*	a		(0,1)(0,1)
E	SAME		aaaaaa		(0,6)(0,6)
E	SAME		ababab		(0,6)(0,6)
E	SAME		bababa		(0,6)(0,6)
E	SAME		b		(0,1)(0,1)

  // CHECK-SAME: channel_handle = #mhlo.channel_handle
  // CHECK-SAME: handle =
  // CHECK-SAME: type = 3
  // CHECK-SAME: is_host_transfer = true
  // CHECK-SAME: mhlo.frontend_attributes = {_xla_host_transfer_handler_name = "tf_rendezvous", _xla_host_transfer_rendezvous = "recv_key_htod_0"}
  // CHECK-SAME: mhlo.sharding = "\08\01\1A\01\01\22\01\00"
  // CHECK-SAME: (!mhlo.token) -> (tensor<i32>, !mhlo.token)

// CHECK:      %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{
// CHECK-SAME:   dot_dimension_numbers = #mhlo.dot<
// CHECK-SAME:     lhs_batching_dimensions = [0],
// CHECK-SAME:     rhs_batching_dimensions = [0],
// CHECK-SAME:     lhs_contracting_dimensions = [3],
// CHECK-SAME:     rhs_contracting_dimensions = [1]
// CHECK-SAME: >}> : (tensor<3x72x1x2048xf32>, tensor<3x2048x512xf32>) -> tensor<3x72x1x512xf32>

// CHECK-SAME: batch_group_count = 1 : i64
// CHECK-SAME: dimension_numbers = "\10\03\1A\02\01\02 \02(\032\02\00\01@\03J\02\01\02"
// CHECK-SAME: explicit_padding = []
// CHECK-SAME: feature_group_count = 1 : i64
// CHECK-SAME: lhs_dilation = [1, 1]
// CHECK-SAME: padding = "VALID"
// CHECK-SAME: rhs_dilation = [2, 2]
// CHECK-SAME: rhs_quantization_axis = -1 : i64

    // CHECK-SAME:   module = ""
    // CHECK-SAME:   platforms = []
    // CHECK-SAME:   version = 5
    // CHECK-SAME:   _entry_function = @main_0
    // CHECK-SAME:   _stablehlo_module_attrs = {}

    %0 = "tf.XlaCallModule"(%arg0, %arg1) {Sout = [#tf_type.shape<?>], dim_args_spec = [], _entry_function = @main_0, module = "", platforms = [], version = 5 : i64} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
    // CHECK: return %[[RESULT]]

  // CHECK:  "tf.BatchFunction"
  // CHECK-SAME: allowed_batch_sizes = [6]
  // CHECK-SAME: batch_timeout_micros = 100000 : i64
  // CHECK-SAME: batching_queue = ""
  // CHECK-SAME: container = ""
  // CHECK-SAME: enable_large_batch_splitting = false
  // CHECK-SAME: max_batch_size = 6 : i64
  // CHECK-SAME: max_enqueued_batches = 10 : i64
  // CHECK-SAME: num_batch_threads = 2 : i64
  // CHECK-SAME: shared_name = "batch/"

  } : (tensor<1x4xf32>) -> (tensor<1x8xf32>)
  func.return %0 : tensor<1x8xf32>

  //       CHECK: %0 = "tfl.custom"(%arg0) <{
  //  CHECK-SAME:   custom_code = "custom_call.my_custom_op",
  //  CHECK-SAME:   custom_option = #tfl<const_bytes : "0x746869735F69735F615F746573745F737472696E67">
  //  CHECK-SAME: }> : (tensor<1x4xf32>) -> tensor<1x8xf32>
}

// CHECK-LABEL: mhlo_custom_call_test__dont_legalize_dict_backend_config

// CHECK-SAME: f = @composite_conv2d_fn_2}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%[[PARTITIONEDCALL_0]], %[[CONST_0]])
// CHECK: %[[RELU6_0:.*]] = "tf.Relu6"(%[[BIASADD_0]])
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %[[CONST_1]])
// CHECK-SAME: f = @composite_conv2d_fn_1

//CHECK: %0 = "vhlo.custom_call_v1"(%arg0, %arg1) <{
//CHECK-SAME: api_version = #vhlo<api_version_v1 API_VERSION_ORIGINAL>,
//CHECK-SAME: backend_config = #vhlo.string_v1<"">,
//CHECK-SAME: call_target_name = #vhlo.string_v1<"tf.ResizeBilinear">,
//CHECK-SAME: called_computations = #vhlo.array_v1<[]>,
//CHECK-SAME: has_side_effect = #vhlo.bool_v1<false>,
//CHECK-SAME: operand_layouts = #vhlo.array_v1<[]>,
//CHECK-SAME: output_operand_aliases = #vhlo.array_v1<[]>,

// CHECK:           "mhlo.dot_general"({{.*}}, {{.*}}) <{
// CHECK-SAME:        lhs_batching_dimensions = [0]
// CHECK-SAME:        rhs_batching_dimensions = [0]
// CHECK-SAME:        lhs_contracting_dimensions = [2]
// CHECK-SAME:        rhs_contracting_dimensions = [1]