op: "ParseExample"
  input: "serilaized"
  input: "Const"
  input: "Const_1"
  input: "Const_2"
  attr {
    key: "Ndense"
    value {
      i: 1
    }
  }
  attr {
    key: "Nsparse"
    value {
      i: 0
    }
  }
  attr {
    key: "Tdense"
    value {
      list {
        type: DT_INT64
      }
    }
  }
  attr {
    key: "dense_shapes"

      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
      %outputs_2, %control_3 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<-1> : tensor<i64>} : () -> tensor<i64>

          number_attr: "Nsparse"
        }
        input_arg {
          name: "dense_keys"
          type: DT_STRING
          number_attr: "Ndense"
        }
        input_arg {
          name: "dense_defaults"
          type_list_attr: "Tdense"
        }
        output_arg {
          name: "sparse_indices"
          type: DT_INT64
          number_attr: "Nsparse"
        }

    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>;

      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
      %outputs_2, %control_3 = tf_executor.island wraps "tf.Const"() {device = "", dtype = !tf_type.string, value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>

  %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 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %1 = "mhlo.pad"(%arg0, %0) <{edge_padding_high = dense<0> : tensor<3xi64>, edge_padding_low = dense<1> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>}> : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<11x11x11xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

  %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

  %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) {