%1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>
    func.return %1 : tensor<100x50xf32>
  }

  // CHECK-LABEL: func @SessionInitializerFunction()
  // CHECK-SAME: tf_saved_model.exported_names = ["SessionInitializerFunction"]

    %4 = "tf.Add"(%1, %3) {device = ""} : (tensor<100x50xf32>, tensor<50xf32>) -> tensor<100x50xf32>
    func.return %4 : tensor<100x50xf32>
  }

  func.func @f2(%arg0: tensor<!tf_type.resource<tensor<100x50xf32>>> {tf.resource_name = "dense/kernel"}, %arg1: tensor<!tf_type.resource<tensor<50xf32>>> {tf.resource_name = "dense/bias"}) -> (tensor<100x50xf32> {tf_saved_model.index_path = ["dense_2"]})

    %4 = "tf.Add"(%1, %3) {device = ""} : (tensor<100x50xf32>, tensor<50xf32>) -> tensor<100x50xf32>
    func.return %4 : tensor<100x50xf32>
  }
}

// -----

// Test case: No errors on no resource arguments.
module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

	// CHECK-LABEL: @serving_default
  func.func @serving_default() -> (tensor<100x50xf32> {tf_saved_model.index_path = ["dense_2"]})

    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>

    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>
    func.return %1 : tensor<100x50xf32>
  }

func.func @testSoftmax(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.softmax"(%arg0) {beta = 1.000000e+00 : f32} : (tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

func.func @testTanh(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.tanh"(%arg0) : (tensor<10x10xf32>) -> tensor<10x10xf32>

  func.return %0 : tensor<10x10xf32>

func.func @unfold_large_constant_splat() -> (tensor<10x10xf32>, tensor<1000x1000xf32>) {
  %0 = arith.constant dense<0.00000e+00> : tensor<10x10xf32>
  %1 = arith.constant dense<1.00000e+00> : tensor<1000x1000xf32>
  func.return %0, %1 : tensor<10x10xf32>, tensor<1000x1000xf32>

  // CHECK-DAG: %cst = arith.constant dense<0.000000e+00> : tensor<10x10xf32>
  // CHECK-DAG: %cst_0 = arith.constant dense<1000> : tensor<2xi64>

# CHECK:"tf.MlirPassthroughOp"
# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"

  %cst = "tf.Const"() {value = dense<1.1> : tensor<100x512xf32>} : () -> tensor<100x512xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>