// Returns true if it is a shaped type of f32 elements.
inline bool IsF32ShapedType(Type t) {
  if (auto shaped_type = mlir::dyn_cast_or_null<ShapedType>(t)) {
    return shaped_type.getElementType().isF32();
  }
  return false;
}

// Return true when the given element_type is QI8.
inline bool IsQI8Type(Type t) {
  auto quantized_type = quant::QuantizedType::getQuantizedElementType(t);

}

bool IsCompatibleTypeWithTFLCastOp(Type type) {
  auto elemType = getElementTypeOrSelf(type);
  // F16, F32, F64, BF16 types are allowed.
  if (elemType.isBF16() || elemType.isF16() || elemType.isF32() ||
      elemType.isF64())
    return true;

  // I1, I4, I8, I16, I32, I64 types are allowed.
  if (elemType.isInteger(1) || elemType.isInteger(4) || elemType.isInteger(8) ||

    Constraint<CPred<"TFL::IsBroadcastableElementsAttrs($0, $1) && TFL::IsDimensionsDegenerateExceptLastOne($1)">>;

def F32ElementsAttr : ElementsAttrBase<
    CPred<"$_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">, "float constant tensor">;
def DefinedByConv2D : Constraint<CPred<"llvm::isa_and_nonnull<mlir::TF::Conv2DOp>($0.getDefiningOp())">>;
// Checks if the value has only one user.
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

        mlir::dyn_cast<SplatElementsAttr>(const_op.getValue());
    if (!splat_elements_attr) {
      return;
    }
    auto element_type = splat_elements_attr.getType().getElementType();
    if (!(element_type.isF32() || element_type.isF16() ||
          element_type.isInteger(1) || element_type.isInteger(32) ||
          element_type.isInteger(64))) {
      return;
    }
    if (splat_elements_attr.getNumElements() *

  std::vector<int> b_size = {};
  std::vector<int> b_map = {};
  PopulateEncodingParams(block_size, &traversal_order, &format, &b_map,
                         &b_size);

  if (type.getElementType().isF32()) {
    tflite::internal::sparsity::FormatConverter<float> format_converter(
        shape, traversal_order, format, b_size, b_map);
    std::vector<float> data;
    data.reserve(type.getNumElements());

             << ")";
      });
      return false;
    }

    // Verify the data type is supported.
    Type element_ty = getElementTypeOrSelf(conv.getType());
    if (!element_ty.isF32() && !element_ty.isF64()) {
      (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) {
        diag << "supported data types for _FusedConv2D are float and double, "
             << " but got " << element_ty;

// is "DT_FLOAT".
inline bool TFTypeIsFloat32Tensor(Value value) {
  auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
  if (!tensorType) return false;
  return tensorType.getElementType().isF32();
}

// Returns true iff the given value is a bf16 tensor.
inline bool TFTypeIsBFloat16Tensor(Value value) {
  auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
  if (!tensorType) return false;

}

bool IsValueWithQuantizablePrecision(Value val) {
  auto type = mlir::dyn_cast<ShapedType>(val.getType());
  if (!type) return false;
  // Supported original tensor data types.
  if (type.getElementType().isF32() || type.getElementType().isBF16())
    return true;
  return false;
}

std::optional<tensorflow::quantization::QuantizationComponentSpec>
GetWeightComponentSpec(

              dq_op->getLoc(), dq_arg_type.clone(qtype.getStorageType()),
              dq_op.getArg());
          inputs.push_back(scast_op.getResult());
        } else if (!elem_type.isF32()) {
          // If the operand is an integer tensor, then it doesn't require the
          // DQ op in the pattern.
          inputs.push_back(operand);
        } else {
          return failure();
        }

    if (!IsQI32Type(input_dequant.getType())) return failure();

    auto output_type =
        mlir::dyn_cast_or_null<ShapedType>(dequant_op.getOutput().getType());
    if (!output_type || !output_type.getElementType().isF32()) return failure();

    auto input_type = mlir::dyn_cast<ShapedType>(input_dequant.getType());
    // TODO(renjieliu): support UniformQuantizedPerAxisType.