%0, %1, %2 ,%3, %4, %5 = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg2, %arg3, %arg4) {T = "tfdtype$DT_BFLOAT16", U = "tfdtype$DT_BFLOAT16", data_format = "NHWC", epsilon = 0.001 : f32, is_training = false} : (tensor<8x8x8x8xbf16>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xbf16>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>,...

    tensor<1x9x9x9x!tf_type.qint8>
  %quant_filter = "tf.Cast"(%filter) {} : (tensor<3x3x9x10xi8>) ->
    tensor<3x3x9x10x!tf_type.qint8>
  %0 = "tf.UniformQuantizedConvolution"(
    %quant_input, %quant_filter, %input_scale, %input_zp,
    %filter_scale, %filter_zp, %accum_scale, %accum_zp
  ) {
    Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32",
    attr_map = "", batch_group_count = 1 : i64,

    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %2:2 = tf_executor.Enter %1#0 frame "while/while_context" : (tensor<i32>) -> (tensor<*xi32>, !tf_executor.control) {T = "tfdtype$DT_INT32"}
    %3:3 = tf_executor.Merge %2#0, %0#0 : tensor<*xi32> {N = 2 : i64, T = "tfdtype$DT_INT32"}

    %0 = "tf.VarHandleOp"() {shared_name = "vocab_file"} : () -> tensor<*x!tf_type.resource<tensor<!tf_type.string>>>
    %1 = "tf.Const"() {value = dense<"assets/vocab_file.txt"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
    "tf.AssignVariableOp"(%0, %1) : (tensor<*x!tf_type.resource<tensor<!tf_type.string>>>, tensor<!tf_type.string>) -> ()

// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>

// CHECK-NEXT:    %[[MAGIC:.*]] = "magic.op"(%[[ADD]], %[[ADD]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
// CHECK-NEXT:    return %[[MAGIC]]

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("^foo")
    // CHECK: name: "fo.o"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("fo{o")
    // CHECK: name: "foo"

    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_BOOL", value = dense<true> : tensor<i1>} : () -> tensor<i1> loc("Cond")
    %2:3 = tf_executor.Switch %0#0, %1#0 : (tensor<i32>, tensor<i1>) -> (tensor<i32>, tensor<i32>, !tf_executor.control) {device = "", T = "tfdtype$DT_INT32"} loc("switch")

    %1:2 = tf_executor.island wraps "tf.StatefulPartitionedCall"(%0#0) {Tin = ["tfdtype$DT_RESOURCE"], Tout = ["tfdtype$DT_RESOURCE"], config = "", config_proto = "", executor_type = "", f = @foo} : (tensor<!tf_type.resource<tensor<8xf32>>>) -> tensor<!tf_type.resource<tensor<8xf32>>> loc("call_foo")
    tf_executor.fetch
  }
  func.return
}

    tf_executor.fetch %0#2 : !tf_executor.control
  }
  func.return
}

// CHECK-LABEL: func @testNextIterationMissingSourceDevice
func.func @testNextIterationMissingSourceDevice() {
  tf_executor.graph {
    // CHECK:      tf_executor.NextIteration.Source
    %0:3 = tf_executor.NextIteration.Source : tensor<i64> {T = "tfdtype$DT_INT64"}