flatbuffers::FlatBufferBuilder* builder) {
  switch (padding) {
    case mlir::TFL::MirrorPaddingType::REFLECT:
      return tflite::MirrorPadMode_REFLECT;
    case mlir::TFL::MirrorPaddingType::SYMMETRIC:
      return tflite::MirrorPadMode_SYMMETRIC;
  }
  llvm_unreachable("invalid mirror_pad_enum in conversion.");
}

static tflite::TensorType ConvertDerivedTypeAttrForOptionWriter(

   *
   * <ul>
   *   <li>{@code equivalent(x, x)} is true (<i>reflexive</i> property)
   *   <li>{@code equivalent(x, y)} and {@code equivalent(y, x)} each return the same result
   *       (<i>symmetric</i> property)
   *   <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code
   *       equivalent(x, z)} is also true (<i>transitive</i> property)
   * </ul>
   *

      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedPerAxisTypeForWeight(
              attr, affine_user.GetQuantizationDimIndex(),
              /*symmetric=*/true, bit_width, is_signed, is_narrow_range,
              is_legacy_float));
    } else {
      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedTypeForWeight(

   *
   * <ul>
   *   <li>{@code equivalent(x, x)} is true (<i>reflexive</i> property)
   *   <li>{@code equivalent(x, y)} and {@code equivalent(y, x)} each return the same result
   *       (<i>symmetric</i> property)
   *   <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code
   *       equivalent(x, z)} is also true (<i>transitive</i> property)
   * </ul>
   *

		setSize = setDriveCount
	} else {
		// Returns possible set counts with symmetry.
		setCounts = possibleSetCountsWithSymmetry(setCounts, argPatterns)

		if len(setCounts) == 0 {
			msg := fmt.Sprintf("No symmetric distribution detected with input endpoints provided %s, drives %d cannot be spread symmetrically by any supported erasure set sizes %d", args, commonSize, setSizes)
			return nil, config.ErrInvalidNumberOfErasureEndpoints(nil).Msg(msg)

    mlir::StringAttr padding) {
  return llvm::StringSwitch<mlir::TFL::MirrorPaddingType>(padding.getValue())
      .Case("REFLECT", mlir::TFL::MirrorPaddingType::REFLECT)
      .Case("SYMMETRIC", mlir::TFL::MirrorPaddingType::SYMMETRIC);
}

#include "tensorflow/compiler/mlir/lite/transforms/generated_legalize_tf.inc"

#define DECL_CONVERT_OP(tf_op)                                               \

			return z
		}

		// x & y == x & y
		z.abs = z.abs.and(x.abs, y.abs)
		z.neg = false
		return z
	}

	// x.neg != y.neg
	if x.neg {
		x, y = y, x // & is symmetric
	}

	// x & (-y) == x & ^(y-1) == x &^ (y-1)
	y1 := nat(nil).sub(y.abs, natOne)
	z.abs = z.abs.andNot(x.abs, y1)
	z.neg = false
	return z
}

// AndNot sets z = x &^ y and returns z.

   *   <li>With finite tolerance, {@code Double.POSITIVE_INFINITY} and {@code
   *       Double.NEGATIVE_INFINITY} are fuzzily equal only to themselves.
   * </ul>
   *
   * <p>This is reflexive and symmetric, but not transitive, so it is not an
   * equivalence relation and not suitable for use in {@link Object#equals}
   * implementations.
   *

   *   <li>With finite tolerance, {@code Double.POSITIVE_INFINITY} and {@code
   *       Double.NEGATIVE_INFINITY} are fuzzily equal only to themselves.
   * </ul>
   *
   * <p>This is reflexive and symmetric, but not transitive, so it is not an
   * equivalence relation and not suitable for use in {@link Object#equals}
   * implementations.
   *

		blockSize *= 2
	}
}

// symMerge{{.FuncSuffix}} merges the two sorted subsequences data[a:m] and data[m:b] using
// the SymMerge algorithm from Pok-Son Kim and Arne Kutzner, "Stable Minimum
// Storage Merging by Symmetric Comparisons", in Susanne Albers and Tomasz
// Radzik, editors, Algorithms - ESA 2004, volume 3221 of Lecture Notes in
// Computer Science, pages 714-723. Springer, 2004.
//
// Let M = m-a and N = b-n. Wolog M < N.