%4 = "tfl.dequantize"(%3) : (tensor<32x12x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x12xf32>
  %5 = "tfl.fully_connected"(%2, %4, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<32x12xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

func.func @NotQuantizeFloatConst() -> tensor<2x2xf32> {
  %0 = arith.constant dense<-0.1> : tensor<2x2xf32>
  %1 = "tfl.quantize"(%0) {qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>} : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>) -> tensor<2x2xf32>
  func.return %2 : tensor<2x2xf32>

// execute the production code path.
func.func @main(%arg0: tensor<2x1xf32>, %arg1: tensor<2x3xf32>) -> (tensor<2x4xf32>) {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<2x1x!quant.uniform<i16:f32, 1.0>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i16:f32, 1.0>>
  %1 = "tfl.dequantize"(%0) : (tensor<2x1x!quant.uniform<i16:f32, 1.0>>) -> (tensor<2x1xf32>)

  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<12x2xf32>} : () -> tensor<12x2xf32>
    %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: [[UNPACK:%.*]]:3 = "tf.Unpack"(%arg0) <{axis = 0 : i64}> : (tensor<3x2x2xf32>) -> (tensor<2x2xf32>, tensor<2x2xf32>, tensor<2x2xf32>)
// CHECK: [[SCALAR_ZERO:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK: [[CONCAT:%.*]] = "tf.Concat"([[SCALAR_ZERO]], [[UNPACK]]#0, [[UNPACK]]#1, [[UNPACK]]#2) : (tensor<i32>, tensor<2x2xf32>, tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>
// CHECK: [[LENGTHS:%.*]] = arith.constant dense<0> : tensor<0xi64>

}

// CHECK-LABEL: prepareAdd
func.func @prepareAdd(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %cst = arith.constant dense<[[0.0, 1.0], [2.0, 255.0]]> : tensor<2x2xf32>
  %add = "tfl.add"(%arg0, %cst) {fused_activation_function="NONE"} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  func.return %add : tensor<2x2xf32>

  // CHECK: %[[v0:.*]] = "tf.Reshape"(%arg1, %[[cst]]) : (tensor<5x6x2xf32>, tensor<2xi64>) -> tensor<5x12xf32>
  // CHECK: %[[v1:.*]] = "tf.BatchMatMulV2"(%arg0, %[[v0]]) <{adj_x = false, adj_y = false}> : (tensor<3x4x5xf32>, tensor<5x12xf32>) -> tensor<3x4x12xf32>
  // CHECK: %[[v2:.*]] = "tf.Reshape"(%[[v1]], %[[cst_1]]) : (tensor<3x4x12xf32>, tensor<4xi64>) -> tensor<3x4x6x2xf32>
  // CHECK: return %[[v2]] : tensor<3x4x6x2xf32>
}

// CHECK-LABEL: testDotToDotGeneralMatrixMatrix
func.func @testDotToDotGeneralMatrixMatrix(%arg0: tensor<2x3072xf32>, %arg1: tensor<3072x512xf32>) -> tensor<2x512xf32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3072xf32>, tensor<3072x512xf32>) -> tensor<2x512xf32>
  func.return %0 : tensor<2x512xf32>

// CHECK:      %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{
// CHECK-SAME:   dot_dimension_numbers = #mhlo.dot<

  %11 = "mhlo.dot"(%10, %5) : (tensor<32x3920xf32>, tensor<3920x10xf32>) -> tensor<32x10xf32>
  %12 = "mhlo.broadcast_in_dim"(%2) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<10xf32>) -> tensor<32x10xf32>
  %13 = mhlo.add %11, %12 : tensor<32x10xf32>
  %14 = mhlo.maximum %13, %0 : tensor<32x10xf32>
  return %14 : tensor<32x10xf32>
}
// CHECK: ReadVariableOp
// CHECK: mhlo.convolution
// CHECK: mhlo.add

  // CHECK-DAG: %[[LOG_SOFTMAX_LOG:.*]] = "tf.Log"(%[[LOG_SOFTMAX_SUM]]) : (tensor<2x1xf32>) -> tensor<2x1xf32>
  // CHECK-DAG: %[[LOG_SOFTMAX:.*]] = "tf.Sub"(%[[LOG_SOFTMAX_SHIFTED]], %[[LOG_SOFTMAX_LOG]]) : (tensor<2x3xf32>, tensor<2x1xf32>) -> tensor<2x3xf32>