// Returns the corresponding type that should be used for performing sum
// accumulation over the given input type.
Type GetSumAccumulationType(Type input_type) {
  MLIRContext *ctx = input_type.getContext();
  if (input_type.isBF16() || input_type.isF16()) return FloatType::getF32(ctx);
  if (input_type.isSignlessInteger(8) || input_type.isSignlessInteger(16))
    return IntegerType::get(ctx, 32);
  return input_type;
}

      if (element_type.isF32()) continue;
      // Element type can either be f32 or bf16 for `SparseMatMulOp` so it
      // must be bf16 here.
      assert(element_type.isBF16());
      Type tensor_type_f32;
      if (tensor_type.hasRank()) {
        tensor_type_f32 = tensorflow::GetTypeFromTFTensorShape(
            tensor_type.getShape(), FloatType::getF32(context));
      } else {

  return false;
}

// Returns true if it is a shaped type of bf16 elements.
inline bool IsBF16ShapedType(Type t) {
  if (auto shaped_type = t.dyn_cast_or_null<ShapedType>()) {
    return shaped_type.getElementType().isBF16();
  }
  return false;
}

// Returns true if it is a shaped type of FloatType elements.
inline bool IsFloatShapedType(Type t) {
  if (auto shaped_type = t.dyn_cast_or_null<ShapedType>()) {

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      Type element_type = t;
      if (auto shaped_type = mlir::dyn_cast<ShapedType>(t)) {
        element_type = shaped_type.getElementType();
      }
      return element_type.isBF16() || element_type.isF32();
    };
    for (Type t : binary_op->getOperandTypes()) {
      if (!is_fusable_type(t)) {
        return false;
      }
    }

    return true;
  }
};

    }
  }

  if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF64()) {
    return tflite::TensorType_FLOAT64;
  } else if (mlir::isa<mlir::TF::StringType>(type)) {
    return tflite::TensorType_STRING;