"a": objectType(map[string]schema.Structural{
					"b": integerType,
					"c": integerType,
					"d": withNullable(true, integerType),
				}),
				"a1": objectType(map[string]schema.Structural{
					"b1": objectType(map[string]schema.Structural{
						"c1": integerType,
					}),
					"d2": objectType(map[string]schema.Structural{
						"e2": integerType,
					}),
				}),
			}),

                                       PatternRewriter *rewriter) {
  // Create the start position of slice. This is done by concatenating
  // `start_index` and `partial_start_position` together.
  IntegerType shape_dtype = rewriter->getIntegerType(32);
  RankedTensorType position_type =
      tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype);
  Value partial_start_position =

                            << zero_points_type << ".\n");
    return failure();
  }

  if (Type zero_points_element_type = zero_points_type.getElementType();
      !mlir::isa<IntegerType>(zero_points_element_type)) {
    LLVM_DEBUG(llvm::dbgs() << "Zero point should be an integer type. Got: "
                            << zero_points_element_type << ".\n");
    return failure();
  }

  auto elem_type = shaped_type.getElementType();
  if (auto float_type = elem_type.dyn_cast<mlir::FloatType>()) {
    TF_ASSIGN_OR_RETURN(value, tfl::ConvertFloatBuffer(shaped_type, buffer));
  } else if (elem_type.isa<mlir::IntegerType>()) {
    TF_ASSIGN_OR_RETURN(value, tfl::ConvertIntBuffer(shaped_type, buffer));
  } else if (elem_type.isa<mlir::TF::StringType>()) {
    tensorflow::TensorProto repr =

		// simply a read on JSON from MinIO and write it back as "parquet".
		//
		// import org.apache.spark.sql.SparkSession
		// import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")