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:   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: %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]

  // CHECK: %[[CONV2D_BACKPROP:[0-9]*]] = "tf.Conv2DBackpropFilter"
  // CHECK-SAME: (%[[IN_TRANSPOSE]], %[[FILTER_PERM]], %[[OUT_BP_TRANSPOSE]])
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: dilations = [1, 4, 2, 3]
  // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 8, 6, 7]
  // CHECK-SAME: (tensor<1x3x32x32xf32>, tensor<4xi32>, tensor<1x8x32x32xf32>)

# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: tf_saved_model.session_initializer
# CHECK-SAME: initializers = [@[[restore:.*]]]

# CHECK: "tf_saved_model.asset"()
# CHECK-SAME: <{filename = [[filename:.*]], sym_name = "[[sym_name:.*]]"}> : () -> ()

# itself is verified in the common graphdef converter, so here just assert
# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: "tf_saved_model.global_tensor"()
# CHECK: "tf_saved_model.session_initializer"() <{initializers = [@[[INIT_FUNC:[a-zA-Z_0-9]+]]]}> : () -> ()

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[OP_A]], %[[OP_B]], %[[OP_C]]] as %{{[a-z0-9]*}}: tensor<*xi1>
// CHECK-SAME: [%[[OP_D]], %[[OP_E]], %[[OP_F]]] as %{{[a-z0-9]*}}: tensor<*xi32>
// CHECK-SAME: [%[[OP_G]], %[[OP_H]], %[[OP_I]]] as %{{[a-z0-9]*}}: tensor<*xf32>
// CHECK-SAME: %[[OP_J]] as %{{[a-z0-9]*}}: tensor<*xi8>
// CHECK-SAME: %[[OP_K]] as %{{[a-z0-9]*}}: tensor<*xi16>
// CHECK-SAME: %[[OP_L]] as %{{[a-z0-9]*}}: tensor<*xi64>