private final String question;
    private final int minValue;
    private final int defaultValue;

    public IntQuestionPromptEvent(long timestamp, String question, int minValue, int defaultValue) {
        super(timestamp);
        this.question = question;
        this.minValue = minValue;
        this.defaultValue = defaultValue;
    }

    public String getQuestion() {

namespace mlir {
namespace TFL {

template <class TFFakeQuantOp>
struct FetchMinMaxAttrs {
  using AttrType = FloatAttr;
  bool operator()(TFFakeQuantOp tf_op, AttrType &min_value,
                  AttrType &max_value) const {
    min_value = tf_op.getMinAttr();
    max_value = tf_op.getMaxAttr();
    return true;  // Successfully matched and fetched.
  }
};

template <class TFFakeQuantOp>

  private static final double[] VALUES = {
    Double.NEGATIVE_INFINITY,
    -Double.MAX_VALUE,
    (double) Long.MIN_VALUE,
    (double) Integer.MIN_VALUE,
    -Math.PI,
    -1.0,
    -Double.MIN_VALUE,
    -0.0,
    +0.0,
    Double.MIN_VALUE,
    1.0,
    Math.PI,
    (double) Integer.MAX_VALUE,
    (double) Long.MAX_VALUE,
    Double.MAX_VALUE,
    Double.POSITIVE_INFINITY,

namespace mlir {
namespace quant {

template <class TFFakeQuantOp>
struct FetchMinMaxAttrs {
  using AttrType = FloatAttr;
  bool operator()(TFFakeQuantOp tf_op, AttrType &min_value,
                  AttrType &max_value) const {
    min_value = tf_op.getMinAttr();
    max_value = tf_op.getMaxAttr();
    return true;  // Successfully matched and fetched.
  }
};

template <class TFFakeQuantOp>

                                                     &histogram_output));
    if (num_bins > 0) {
      const float min_value = min_output->scalar<float>()();
      const float max_value = max_output->scalar<float>()();
      CalculateHistogramStatistics(context, input_tensor, min_value, max_value,
                                   num_bins, histogram_output);
    }
  }

 private:
  std::string id_;

// number of bins might be smaller than the given `num_bins`.
inline float CalculateBinWidth(const float min_value, const float max_value,
                               const int32_t num_bins) {
  const float raw_bin_width = (max_value - min_value) / num_bins;
  return std::pow(2, std::ceil(std::log2(raw_bin_width)));
}

// Calculates the lower bound of the histogram. The lower bound is in form of

  private static final double[] VALUES = {
    Double.NEGATIVE_INFINITY,
    -Double.MAX_VALUE,
    (double) Long.MIN_VALUE,
    (double) Integer.MIN_VALUE,
    -Math.PI,
    -1.0,
    -Double.MIN_VALUE,
    -0.0,
    +0.0,
    Double.MIN_VALUE,
    1.0,
    Math.PI,
    (double) Integer.MAX_VALUE,
    (double) Long.MAX_VALUE,
    Double.MAX_VALUE,
    Double.POSITIVE_INFINITY,

        "byte[]"                             | "[Byte.MIN_VALUE, Byte.MAX_VALUE]"        | "[-128, 127]"
        "short[]"                            | "[Short.MIN_VALUE, Short.MAX_VALUE]"      | "[-32768, 32767]"
        "int[]"                              | integerArray()                            | "[-2147483648, 2147483647]"

// mapindex   <expr> (               <mode>       of type <typ>)
//
// value      <expr> (<untyped kind> <mode>                    )
// value      <expr> (               <mode>       of type <typ>)
//
// nilvalue   untyped nil
// nilvalue   nil    (                            of type <typ>)
//
// commaok    <expr> (<untyped kind> <mode>                    )
// commaok    <expr> (               <mode>       of type <typ>)
//

// CHECK-LABEL: @fuseBroadcastMulIntoFullyConnected
func.func @fuseBroadcastMulIntoFullyConnected(%arg0: tensor<1x10368xbf16>) -> tensor<32x1x256xbf16> {
  %cst_0 = arith.constant dense<2.0> : tensor<256x10368xbf16>
  %cst_1 = "tfl.no_value"() {value = unit} : () -> none
  %cst_2 = arith.constant dense<3.0> : tensor<32x1x256xbf16>
  %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst_1) {