%dimension = "tf.Const"() { value = dense<3> : tensor<1xi64> } : () -> tensor<1xi64>
    %6 = "tf.Sum"(%3, %dimension) { keep_dims = true }: (tensor<1x3x2x2xf32>, tensor<1xi64>) -> tensor<1x3x2x1xf32>
    return %5, %6 : tensor<1x3x2x2xf32>, tensor<1x3x2x1xf32>
  }

// CHECK-LABEL: func @conv_with_multiple_uses

  %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
  %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>
  // expected-error @+1 {{Node `mhlo.dynamic_iota` must have compile-time constant}}
  %3 = "mhlo.dynamic_iota"(%2) <{iota_dimension = 0 : i64}> : (tensor<1xi64>) -> tensor<?xi32>
  %4 = mhlo.multiply %3, %3 : tensor<?xi32>
  return %4 : tensor<?xi32>

    %0 = "tf.XlaCallModule"(%output, %cst) <{Sout = [#tf_type.shape<10x1x3>], dim_args_spec = [], disabled_checks = [], function_list = [], has_token_input_output = false, module = "", platforms = ["CPU"], version = 9 : i64}> {_entry_function...

  EXPECT_THAT(num_bytes, Eq(16));
}

TEST_F(GetSizeInBytesTest, Float64ConstOpSizeInBytes) {
  constexpr absl::string_view kConstOpExpr =
      R"mlir(%cst = "tf.Const"() {value = dense<3.0> : tensor<2xf64>} : () -> tensor<2xf64>)mlir";

  Block block{};
  TF::ConstOp int_tensor_const_op = ParseConstOp(kConstOpExpr, block, ctx_);

  const int64_t num_bytes = GetSizeInBytes(int_tensor_const_op);
  EXPECT_THAT(num_bytes, Eq(16));

# CHECK: operandSegmentSizes = array<i32: 1, 1, 0, 1, 1>
# CHECK: resultSegmentSizes = array<i32: 0, 0, 0, 1>
# CHECK: tf_executor.fetch %[[output]] : tensor<*xi64>

node {
  name: "serilaized"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

using func::FuncOp;
using mlir::ModuleOp;

static constexpr char kMlirModuleStr[] = R"(
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1442 : i32}} {

# CHECK: resultSegmentSizes = array<i32: 2, 2, 2, 2, 0, 0>
# CHECK: tf_executor.fetch %[[parse_example]]#0, %[[parse_example]]#7 : tensor<*xi64>, tensor<*xf32>

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "shape"
    value {
      shape {

  %7 = "mhlo.broadcast_in_dim"(%3) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<5xf32>) -> tensor<32x28x28x5xf32>
  %8 = mhlo.add %6, %7 : tensor<32x28x28x5xf32>
  %9 = mhlo.maximum %8, %1 : tensor<32x28x28x5xf32>
  %10 = "mhlo.reshape"(%9) : (tensor<32x28x28x5xf32>) -> tensor<32x3920xf32>

    func.func @main() -> () {
      %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
      %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
      func.return
    }
  })";

  CellReader<int64_t> dynamism_counter(kDynamismOpCounterStreamzName);
  CellReader<int64_t> dynamism_function_counter(
      kDynamismFunctionCounterStreamzName);