Variadic<TensorOf<[TF_Float32, TF_Int64, TF_Str]>>:$dense_values    // len(Tdense)
  );

  TF_DerivedOperandSizeAttr Nsparse = TF_DerivedOperandSizeAttr<2>;
  TF_DerivedOperandSizeAttr Ndense = TF_DerivedOperandSizeAttr<3>;
  TF_DerivedOperandTypeListAttr Tdense = TF_DerivedOperandTypeListAttr<4>;
  TF_DerivedResultTypeListAttr sparse_types = TF_DerivedResultTypeListAttr<1>;

  %value = "tf.Const"() {device = "/device:CPU:0", value = dense<0.1> : tensor<1x512xf32>} : () -> (tensor<1x512xf32>)
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)
  %size = "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<i32>} : () -> (tensor<i32>)
  %indices = "tf.Const"() {device = "/device:CPU:0", value = dense<[0]> : tensor<1xi32>} : () -> (tensor<1xi32>)

  %0 = arith.constant dense<-1.0> : tensor<f32>
  %1 = arith.constant dense<1.0> : tensor<f32>
  %2 = arith.constant dense<1.0> : tensor<f32>
  %3 = arith.constant dense<1.0> : tensor<f32>
  %4 = arith.constant dense<4.0> : tensor<f32>
  %5 = arith.constant dense<4.0> : tensor<f32>
  %6 = arith.constant dense<2.0> : tensor<f32>

  // CHECK-DAG: [[cst0:%.*]] = arith.constant dense<1.000000e+00> : tensor<f32>

  %0 = "quantfork.stats"(%arg0) {
    layerStats = dense<[-1.28e-5, 1.27e-5]> : tensor<2xf32>
  } : (tensor<1x5x5x2xf32>) -> tensor<1x5x5x2xf32>
  %w = arith.constant dense<[[[[-1.0, 1.0]]], [[[1.0, 2.0]]], [[[-2.0, 1.0]]]]> : tensor<3x1x1x2xf32>
  %b = arith.constant dense<0.0> : tensor<3xf32>
  %b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {

// CHECK-NEXT:     [[PREFIX:%.*]] = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
// CHECK-NEXT:     [[SLICE:%.*]] = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>
// CHECK-NEXT:     [[NAME:%.*]] = "tf.Const"() <{value = dense<"y"> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>

  %in = arith.constant dense<0.0> : tensor<3x3x48x1xf32>
  %min = arith.constant dense<0.0> : tensor<48xf32>
  %max = arith.constant dense<255.0> : tensor<48xf32>
  %mini = "tf.Identity"(%min) : (tensor<48xf32>) -> tensor<48xf32>
  %maxi = "tf.Identity"(%max) : (tensor<48xf32>) -> tensor<48xf32>
  %s1 = arith.constant dense<[3, 3, 48]> : tensor<3xi32>
  %s2 = arith.constant dense<[3, 3, 48, 1]> : tensor<4xi32>

  %in = arith.constant dense<0.0> : tensor<3x3x48x1xf32>
  %min = arith.constant dense<0.0> : tensor<48xf32>
  %max = arith.constant dense<15.0> : tensor<48xf32>
  %mini = "tf.Identity"(%min) : (tensor<48xf32>) -> tensor<48xf32>
  %maxi = "tf.Identity"(%max) : (tensor<48xf32>) -> tensor<48xf32>
  %s1 = arith.constant dense<[3, 3, 48]> : tensor<3xi32>
  %s2 = arith.constant dense<[3, 3, 48, 1]> : tensor<4xi32>

  %cst = "tf.Const"() {value = dense<[[[[-8.69931221, 6.44628429], [-9.18393421, 1.53671741], [8.68561744, -3.581774]]]]> : tensor<1x1x3x2xf32>} : () -> tensor<1x1x3x2xf32>
  %cst_0 = "tf.Const"() {value = dense<-1.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

// CHECK-DAG:       [[VAL_2:%.*]] = "tfl.pseudo_const"{{.*}}dense<0> : tensor<4xi32>
// CHECK-DAG:       [[VAL_3:%.*]] = "tfl.pseudo_const"{{.*}}dense<[0, 0, 0, 256]> : tensor<4xi32>
// CHECK-DAG:       [[VAL_4:%.*]] = "tfl.pseudo_const"{{.*}}dense<[0, 0, 0, 768]> : tensor<4xi32>
// CHECK-DAG:       [[VAL_5:%.*]] = "tfl.pseudo_const"{{.*}}dense<[-1, -1, -1, 256]> : tensor<4xi32>

  %1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>