* The key reference queue contains entries whose keys have been garbage collected, and which
     * need to be cleaned up internally.
     */
    @CheckForNull final ReferenceQueue<K> keyReferenceQueue;

    /**
     * The value reference queue contains value references whose values have been garbage collected,
     * and which need to be cleaned up internally.
     */
    @CheckForNull final ReferenceQueue<V> valueReferenceQueue;

     * The key reference queue contains entries whose keys have been garbage collected, and which
     * need to be cleaned up internally.
     */
    @CheckForNull final ReferenceQueue<K> keyReferenceQueue;

    /**
     * The value reference queue contains value references whose values have been garbage collected,
     * and which need to be cleaned up internally.
     */
    @CheckForNull final ReferenceQueue<V> valueReferenceQueue;

  // consistency of the argument and result types.

  // Validate dense variadic input and output lengths.
  // NOTE(mrry): The Tdense attr is derived from dense_defaults, so we
  // do not need to validate dense_defaults.
  auto dense_types_count =
      std::distance(op.getTdense().begin(), op.getTdense().end());
  auto dense_values_count =
      std::distance(op.getDenseValues().begin(), op.getDenseValues().end());

		}
	} else {
		// PV has already been expanded and hence we can be here for following reasons:
		//   1. If expansion is pending on the node and this was just a spurious update event
		//      we don't need to do anything and let kubelet handle it.
		//   2. It could be that - although we successfully expanded the volume, we failed to
		//      record our work in API objects, in which case - we should resume resizing operation

  const RankedTensorType filter_ty =
      filter.getType().dyn_cast_or_null<RankedTensorType>();
  // If indeed both input type & filter type are ranked type and have ranks.
  // We will need to check their ranks are valid.
  if ((input_ty && input_ty.hasRank() && input_ty.getRank() != 4) ||
      (filter_ty && filter_ty.hasRank() && filter_ty.getRank() != 4)) {

	Addralign uint32
}

/*
 * Relocation entries.
 */

// ELF32 Relocations that don't need an addend field.
type Rel32 struct {
	Off  uint32 /* Location to be relocated. */
	Info uint32 /* Relocation type and symbol index. */
}

// ELF32 Relocations that need an addend field.
type Rela32 struct {
	Off    uint32 /* Location to be relocated. */

  // BatchMatMul should have exactly the same batch dimensions and
  // BatchMatMulV2 should have broadcastable batch dimensions.
  //
  // The last two dimensions are non-batch dimensions that don't need to
  // participate in batch dimension compatibility check.
  if (std::is_same<OpT, BatchMatMulOp>()) {
    for (const auto& dim_pairs : llvm::zip(x_batches, y_batches)) {
      int64_t x_dim = std::get<0>(dim_pairs);

  |   be done with a regex, but we need inter-field validation and rules which could be
  |   dealt with by something like drools.
  |
  | o i18n: would be good to be able to have names/descriptions/specifications
  |   in as many languages as possible. (see MNG-3626)
  |
  | o annotation mechanism so that changes to the model can be accurately tracked.
  |

        .addHeader("Content-Encoding: gzip")
        .body(gzip("b"))
        .build(),
    )

    // Seed the pool with a bad connection.
    assertContent("a", getResponse(newRequest("/")))

    // Give the server time to disconnect.
    Thread.sleep(500)

    // This connection will need to be recovered. When it is, transparent gzip should still work!
    assertContent("b", getResponse(newRequest("/")))

	// The Copy method will do the right thing if copy returns nil
	// for some components of an AST but not others, so a good
	// copy function will only return non-nil for AST values that
	// need to change.
	// Copy itself returns either a copy or nil.
	Copy(copy func(AST) AST, skip func(AST) bool) AST

	// Implement the fmt.GoStringer interface.
	GoString() string
	goString(indent int, field string) string
}