return false;
    if (mlir::isa<FloatType>(element_type)) {
      auto value = *attr.value_begin<APFloat>();
      return value.isNegative() && value.isInfinity();
    } else if (element_type.isInteger(1)) {
      auto value = *attr.value_begin<APInt>();
      return value.isZero();
    } else {
      auto value = *attr.value_begin<APInt>();
      return element_type.isUnsignedInteger() ? value.isMinValue()

  if (!matchPattern(iota, m_Constant(&iota_const_attr))) return false;

  auto iota_type = iota_const_attr.getType();
  auto iota_shape = iota_type.getShape();
  auto reduce_dim = (*dimensions.value_begin<APInt>()).getSExtValue();
  if (reduce_dim < 0) reduce_dim += iota_type.getRank();

  auto index =
      std::optional<SmallVector<int64_t>>(std::in_place, iota_type.getRank());
  while (index.has_value()) {

    if (!cond_type || !cond_type.getShape().equals({}) ||
        !cond_type.getElementType().isInteger(/*width=*/1))
      return failure();

    // Identify the branch to inline.
    bool cond_value = (*cond.value_begin<APInt>()).getSExtValue();
    func::FuncOp func = cond_value ? then_func : else_func;

    // Make sure that the function has exactly one block to simplify inlining.

    }

    if (alg_constant.getNumElements() != 1) {
      return rewriter.notifyMatchFailure(op, "expected alg to be a scalar");
    }

    uint64_t alg_value = ((*alg_constant.value_begin<APInt>()).getZExtValue());
    tensorflow::ConcreteRngAlgorithm alg;
    if (tensorflow::RNG_ALG_PHILOX == alg_value) {
      alg = tensorflow::ConcreteRngAlgorithm::RNG_ALG_PHILOX;

      return false;
    if (mlir::isa<FloatType>(element_type)) {
      auto value = *attr.value_begin<APFloat>();
      return value.isNegative() && value.isInfinity();
    } else if (element_type.isInteger(1)) {
      auto value = *attr.value_begin<APInt>();
      return value.isZero();
    } else {
      auto value = *attr.value_begin<APInt>();
      return element_type.isUnsignedInteger() ? value.isMinValue()

        new_bias_values.resize(bias_size, *bias_cst.value_begin<APFloat>());
      } else if (bias_cst.getNumElements() == bias_size) {  // 1-d bias, copy it
        new_bias_values.insert(new_bias_values.begin(),
                               bias_cst.value_begin<APFloat>(),
                               bias_cst.value_end<APFloat>());
      } else {
        return failure();

    TFL::TransposeOp::build(
        *builder, result, UnrankedTensorType::get(input_type.getElementType()),
        input, perm);
    return;
  }

  const auto perm_value_it = perm_const.value_begin<APInt>();

  const ArrayRef<int64_t> input_shape = input_type.getShape();
  SmallVector<int64_t, 4> output_shape(input_shape.size());

  for (int i = 0; i < output_shape.size(); ++i) {