return failure();
    }
    for (Value input : op->getOperands()) {
      if (getElementTypeOrSelf(input).isBF16()) {
        return success();
      }
    }
    for (Value value : op->getResults()) {
      if (getElementTypeOrSelf(value).isBF16()) {
        return success();
      }
    }
    return failure();
  }

  Type element_type = shaped_type.getElementType();
  if (element_type.isF16()) {
    auto floatType = mlir::FloatType::getF16(element_type.getContext());
    auto floatAttr = mlir::FloatAttr::get(floatType, static_cast<float>(value));
    std::vector<Attribute> floatValues({floatAttr});
    return DenseElementsAttr::get(shaped_type, floatValues);
  } else if (element_type.isBF16()) {
    auto floatType = mlir::FloatType::getBF16(element_type.getContext());

  }
}

Status ConvertScalarTypeToDataType(Type type, DataType* dtype) {
  if (type.isF16()) {
    *dtype = DT_HALF;
    return absl::OkStatus();
  } else if (type.isF32()) {
    *dtype = DT_FLOAT;
    return absl::OkStatus();
  } else if (type.isF64()) {
    *dtype = DT_DOUBLE;
    return absl::OkStatus();
  } else if (type.isBF16()) {
    *dtype = DT_BFLOAT16;
    return absl::OkStatus();

  if (!tensorType) return false;
  return tensorType.getElementType().isBF16();
}

// Returns true iff the given value is a f16 tensor.
inline bool TFTypeIsHalfTensor(Value value) {
  auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
  if (!tensorType) return false;
  return tensorType.getElementType().isF16();
}

// Returns true iff the given value is a f16 or bf16 tensor.

namespace tflite {

using absl::StatusOr;

namespace errors = tensorflow::errors;

tflite::TensorType ConvertTypeToTensorType(mlir::Type type) {
  if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF64()) {
    return tflite::TensorType_FLOAT64;

  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(
    const tensorflow::quantization::QuantizationOptions& quantization_options) {

def IsF32ElementType : Constraint<
  CPred<"getElementTypeOrSelf($0).isF32()">>;

// Checks if the value has the type of bfloat16.
def IsBF16ElementType : Constraint<
  CPred<"getElementTypeOrSelf($0).isBF16()">>;

// Checks if the value has the type of UniformQuantizedType.
def IsUniformQuantizedType : Constraint<
  CPred<"getElementTypeOrSelf($0).isa<mlir::quant::UniformQuantizedType>()">>;

    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() *

      return failure();
    }
    Type input_element_type = getElementTypeOrSelf(dequantize_op.getInput());
    // Most passthrough ops do not support F16.
    if (input_element_type.isF16()) {
      return failure();
    }

    // Set the output type of the dequantize op and push it down.
    dequantize_op.getOutput().setType(output_type);
    passthrough_op->replaceAllUsesWith(dequantize_op);