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() *

#include "tsl/platform/statusor.h"

namespace mlir {
namespace TFL {

absl::StatusOr<TypedAttr> CreateTypedAttr(ShapedType shaped_type, int value) {
  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 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);

    format_converter.DenseToSparse(data.data());
    sparsity =
        GetSparsity(type.getNumElements() - format_converter.GetData().size(),
                    type.getNumElements());
  } else if (type.getElementType().isF16()) {
    tflite::internal::sparsity::FormatConverter<Eigen::half> format_converter(
        shape, traversal_order, format, b_size, b_map);
    std::vector<Eigen::half> data;
    data.reserve(type.getNumElements());

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.
inline bool TFTypeIsBFloat16OrHalfTensor(Value value) {
  return TFTypeIsBFloat16Tensor(value) || TFTypeIsHalfTensor(value);
}

  switch (elem_type.getIntOrFloatBitWidth()) {
    case 16: {
      assert(bytes_len % 2 == 0);
      // Supports both BF16 and F16.
      assert(elem_type.isF16() || elem_type.isBF16());
      int elem_count = bytes_len / 2;

      if (elem_type.isF16()) {
        std::vector<Eigen::half> values;
        values.reserve(elem_count);

        const char* data = reinterpret_cast<const char*>(buffer.data());

      return errors::Unimplemented(absl::StrCat(
          "Converting DataType '", DataTypeString(dtype), "' to MLIR Type"));
  }
}

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;

#include "tensorflow/core/platform/errors.h"

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()) {

      const ShapedType new_result_type =
          mlir::dyn_cast<ShapedType>(convert_op.getType());

      // Proceeds only if the converting is to float16.
      if (!new_result_type.getElementType().isF16()) continue;

      // Convert values.
      std::vector<Eigen::half> new_values;
      const DenseFPElementsAttr value_attr =
          mlir::cast<DenseFPElementsAttr>(op.getValue());

}

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) ||