%18 = "tf.BiasAdd"(%17, %arg4) {data_format = "NHWC"} : (tensor<2x1001xf32>, tensor<1001xf32>) -> tensor<2x1001xf32>
    %19 = "tf.Reshape"(%18, %7) : (tensor<2x1001xf32>, tensor<2xi32>) -> tensor<2x1001xf32>
    %loss, %backprop = "tf.SparseSoftmaxCrossEntropyWithLogits"(%19, %13) : (tensor<2x1001xf32>, tensor<2xi64>) -> (tensor<2xf32>, tensor<2x1001xf32>)

module {
  func.func @not_quantize_const() -> (tensor<2x1024xf32>) {
    // Nothing happens if not connected wiht quantizable op.
    %cst_0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    func.return %cst_0: tensor<2x1024xf32>
  }

// CHECK-LABEL: func @not_quantize_const
// CHECK-DAG: %[[W:.*]] = "tf.Const"() <{value = dense<2.000000e+00> : tensor<2x1024xf32>
// CHECK: return %[[W]] : tensor<2x1024xf32>
}

  // CHECK: [[TOKEN:%.*]] = mhlo.create_token : !mhlo.token
  %0 = "mhlo.create_token"() : () -> !mhlo.token

  // CHECK:               [[INFEED:%.*]]:3 = "mhlo.infeed"([[TOKEN]]) <{
  // CHECK-SAME{LITERAL}:   infeed_config = "", layout = [[1, 3, 2, 0], [1, 2, 0]]
  // CHECK-SAME:          }> : (!mhlo.token) -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>, !mhlo.token)

    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK: %[[cst:.*]] = "arith.constant"() <{value = dense<0.000000e+00> : tensor<2x1024xf32>}> : () -> tensor<2x1024xf32>

    func.return %2 : tensor<2x100xf32>
  }
  func.func @func_2_CPU_FLOAT(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.cost = 2.000000e+01 : f32, tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} {

    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

}

func.func @matmul_batchv3(%arg0: tensor<2x10x15xf32>, %arg1: tensor<15x17xf32>) -> tensor<2x10x17xf32> {
  %0 = "tf.BatchMatMulV3"(%arg0, %arg1) {Ta = "tfdtype$DT_FLOAT", Tb = "tfdtype$DT_FLOAT",device = "/device:CPU:0", name = "MatMul", adj_x = false, adj_y = false} :
(tensor<2x10x15xf32>, tensor<15x17xf32>) -> tensor<2x10x17xf32>

  %44 = "tf.AddV2"(%43, %37) {device = ""} : (tensor<2x16x1xf32>, tensor<2x16x1xf32>) -> tensor<2x16x1xf32>
  %45 = "tf.Mul"(%42, %35) {device = ""} : (tensor<2x16x1xf32>, tensor<2x16x1xf32>) -> tensor<2x16x1xf32>
  %46 = "tf.AddV2"(%45, %34) {device = ""} : (tensor<2x16x1xf32>, tensor<2x16x1xf32>) -> tensor<2x16x1xf32>

    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
    %1 = "tf.Cast"(%0) {Truncate = false} : (tensor<2x1024xi8>) -> tensor<2x1024xf32>
    %2 = "tf.MatMul"(%arg0, %1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x2xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    %3 = "tf.Mul"(%2, %cst_0) : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>
    return %3 : tensor<*xf32>
  }