%cst = arith.constant dense<3> : tensor<1xi32>
  %0 = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<?x3xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<3xf32>>>
  %1 = "tf.TensorListStack"(%0, %cst) {num_elements = 2 : i64} : (tensor<!tf_type.variant<tensor<3xf32>>>, tensor<1xi32>) -> tensor<2x3xf32>
  func.return %1 : tensor<2x3xf32>

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]},
# CHECK-SAME:   [[ARG1:%.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @[[VAR]]})
# CHECK-SAME:             -> (tensor<3x3xf32> {tf_saved_model.index_path = ["r"]})

# CHECK-LABEL:      func @key(
# CHECK-SAME:   %[[ARG0:.*]]: tensor<3x5xf32> {tf_saved_model.index_path = ["y"]}
# CHECK-SAME:   %[[ARG1:.*]]: tensor<5x3xf32> {tf_saved_model.index_path = ["x"]}
# CHECK-SAME:                  tensor<3x3xf32> {tf_saved_model.index_path = ["t"]}
# CHECK-SAME:                  tensor<5x5xf32> {tf_saved_model.index_path = ["s"]}
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]

    module attributes {} {
      func.func @main(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> attributes {} {
        %0 = stablehlo.constant dense<1.0> : tensor<4x3xf32>
        %1 = stablehlo.dot_general %arg0, %0, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32>
        return %1 : tensor<1x3xf32>
      }
    }
  )mlir");
  ASSERT_TRUE(module_op);

    %cst_0 = arith.constant dense<1.0> : tensor<5x3xf32>
    %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x3xf32>, tensor<5x3xf32>, tensor<5xf32>) -> tensor<?x5xf32>
    func.return %0, %0 : tensor<?x5xf32>, tensor<?x5xf32>
  }

  // CHECK-DAG: %[[EXP:.*]] = "tf.Exp"(%[[SHIFTED]]) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  // CHECK-DAG: %[[SUM:.*]] = "tf.Sum"(%[[EXP]], %[[AXIS]]) <{keep_dims = true}> : (tensor<2x3xf32>, tensor<1xi64>) -> tensor<2x1xf32>
  // CHECK-DAG: %[[RESULT:.*]] = "tf.Div"(%[[EXP]], %[[SUM]]) : (tensor<2x3xf32>, tensor<2x1xf32>) -> tensor<2x3xf32>
  // CHECK: return %[[RESULT]]

}

func.func @addN(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> {
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: addN
// CHECK:  "tfl.add_n"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:  return
}

func.func @testSoftmaxCrossEntropyWithLogits(%arg0: tensor<2x3xf32>, %arg1: tensor<3xf32>) -> (tensor<3xf32>, tensor<2x3xf32>) {
  %0:2 = "tf.SoftmaxCrossEntropyWithLogits"(%arg0, %arg1) : (tensor<2x3xf32>, tensor<3xf32>) -> (tensor<3xf32>, tensor<2x3xf32>)
  func.return %0#0, %0#1 : tensor<3xf32>, tensor<2x3xf32>
}

// -----

func.func @testSoftmaxCrossEntropyWithLogits(%arg0: tensor<2xf32>, %arg1: tensor<3xf32>) -> (tensor<3xf32>, tensor<3xf32>) {

  %8 = "tfl.concatenation"(%2, %0) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x1xf32>, tensor<1x1xf32>) -> tensor<1x2xf32>
  %9 = "quantfork.stats"(%8) {layerStats = dense<[-0.488159984, 0.189515018]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  %10 = "tfl.concatenation"(%9, %7) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<1x4xf32>

// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, none, none, none, none) -> tensor<1x3xf32>
// CHECK:           [[VAL_50:%.*]] = tensor.cast [[VAL_51:%.*]] : tensor<1x3xf32> to tensor<1x?xf32>