// CHECK:      name: "ParseExample"
      // CHECK-NEXT: op: "ParseExampleV2"
      // CHECK-NEXT: input: "input0"

  // CHECK-LABEL: func @init_all_tables_with_explicit_vocab_size
  // CHECK-DAG: [[CST:%.*]]  = arith.constant dense<["apple", "banana"]> : tensor<2x!tf_type.string>
  // CHECK-DAG: [[CST_0:%.*]]  = arith.constant dense<[0, 1]> : tensor<2xi64>
  // CHECK: [[VAL:%.*]] = "tf.HashTableV2"()
  // CHECK: "tf.LookupTableImportV2"([[VAL]], [[CST]], [[CST_0]])

    -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>, tensor<2xi64>, tensor<2xi64>, tensor<32xf32>, tensor<32xf32>)

  // CHECK: tfrt.return {{.*}}, [[outputs]]#0
  func.return %outputs#0 : tensor<?x2xi64>
}

// CHECK-LABEL: func @no_native

}

// Decompose tf.RngReadAndSkip.
//
// For Philox, the resource variable holds a tensor<3xi64> with the state:
//   [counter_lo, counter_hi, key]
//
//   RngReadAndSkip increments the 128 bit counter value by 256 * delta and
//   returns the original state value.
//
// For Threefry, the resource variable holds a tensor<2xi64> with the state:
//   [counter, key]
//

  // CHECK-DAG: %[[CST1:.*]] =  "tf.Const"() <{value = dense<[8, 6, 6, 18]> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x8x1x1x1x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x1x1x18xbf16>
  // CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<8x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x6x6x18xbf16>

      : (tensor<?x!tf_type.string>, tensor<0x!tf_type.string>, tensor<0x!tf_type.string>, tensor<1x!tf_type.string>, tensor<0x!tf_type.string>, tensor<0xi64>)
      -> tensor<?xi64>

  func.return %dense_value : tensor<?xi64>

// CHECK: return %[[GATHER]] : tensor<1x300x10xf32>

// -----

module {
  func.func @propagate_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst = "tf.Const"() {value = dense<[1, 1, 300]> : tensor<3xi64>} : () -> tensor<3xi64>

  // CHECK-NEXT:  return [[FIND]] : tensor<*xi64>
  func.return %1 : tensor<*xi64>
}

// -----

// Test for case with int64 -> string hashtable.
func.func @hashtable_int64_to_string(%arg0: tensor<i64>) -> tensor<*x!tf_type.string> {
  %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])
  // CHECK: %[[R1:.*]] = "tf.BiasAdd"(%[[R0]], %[[ARG1]]) <{data_format = "NHWC"}> {device = ""}
  // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]])

  %2 = "mhlo.dynamic_broadcast_in_dim"(%cst_0, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %3 = mhlo.multiply %0, %2 : tensor<?x256x256x2xf32>
  %4 = "mhlo.dynamic_broadcast_in_dim"(%cst_1, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %5 = mhlo.add %3, %4 : tensor<?x256x256x2xf32>