func.return %0 : tensor<1024xf32>

  // 1.0 * 2.0 * 512 + 4.0 = 1028.0
  // CHECK: %[[CST:.*]] = arith.constant dense<1.028000e+03> : tensor<1024xf32>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ConstantFoldFullyConnectedNoBias
func.func @ConstantFoldFullyConnectedNoBias() -> tensor<1024xf32> {

    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn_1} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn_1} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

// CHECK-LABEL: func @testCase(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<1x2x3xf32>>>)
func.func @testCase(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32> {
  %0 = "tf.Case"(%arg0, %arg1) {branches = [@branch_0, @branch_1], is_stateless = false} : (tensor<i32>, tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>

  // CHECK: [[Result0:%.*]] = "tf.CaseRegion"

// CHECK-LABEL: func @testWhileCasts(%arg0: tensor<!tf_type.variant<tensor<1x3xf32>>>) -> tensor<!tf_type.variant<tensor<*xf32>>>
func.func @testWhileCasts(%arg0: tensor<!tf_type.variant<tensor<1x3xf32>>>) -> (tensor<!tf_type.variant<tensor<*xf32>>>) {
  %0 = "tf.While"(%arg0) {
    cond = @testWhileCond, body = @testWhileBody, is_stateless = false
  } : (tensor<!tf_type.variant<tensor<1x3xf32>>>) -> (tensor<!tf_type.variant<tensor<*xf32>>>)

func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

  %prelu = "tfl.prelu"(%arg0, %cst) : (tensor<1x10x10x3xf32>, tensor<1x1x3xf32>) -> tensor<1x10x10x3xf32>
  func.return %prelu : tensor<1x10x10x3xf32>

// CHECK: %[[cst:.*]] = arith.constant dense<[{{\[}}[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>

      %2 = "tf.ReadVariableOp"(%arg15) : (tensor<*x!tf_type.resource<tensor<7x7x3x64xf32>>>) -> tensor<7x7x3x64xf32>
      %3 = "tf.ReadVariableOp"(%arg16) : (tensor<*x!tf_type.resource<tensor<1001xf32>>>) -> tensor<1001xf32>
      %4 = "tf.ReadVariableOp"(%arg17) : (tensor<*x!tf_type.resource<tensor<64x1001xf32>>>) -> tensor<64x1001xf32>
      %5 = "tf.ReadVariableOp"(%arg18) : (tensor<*x!tf_type.resource<tensor<f32>>>) -> tensor<f32>

      %arg0 = "tf.Const"() {value = dense<-3.0> : tensor<5x7xf32>} : () -> tensor<5x7xf32>
      %arg1 = "tf.Const"() {value = dense<-3.0> : tensor<11x7xf32>} : () -> tensor<11x7xf32>

      %1 = "tf.MatMul"(%arg0, %arg1) {transpose_a = false, transpose_b = true} : (tensor<5x7xf32>, tensor<11x7xf32>) -> tensor<5x11xf32>

      func.return %1 : tensor<5x11xf32>
    }
  })";
  TF_ASSERT_OK_AND_ASSIGN(

}

// CHECK-LABEL: QuantizeConcat
func.func @QuantizeConcat(tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 1.000000e-01:128>> {
^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<1x2xf32>):
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<2x2xf32>