rsOpts volume.NodeResizeOptions) (bool, error) {

	supportsExpansion, expandableVolumePlugin := og.checkIfSupportsNodeExpansion(volumeToMount)

	if supportsExpansion {
		// lets use sizes handed over to us by caller for comparison
		if rsOpts.NewSize.Cmp(rsOpts.OldSize) > 0 {
			pv := volumeToMount.VolumeSpec.PersistentVolume

    if (!k_values_or.has_value() || !k_indices_or.has_value()) return failure();
    int32_t k_values = k_values_or.value();
    int32_t k_indices = k_indices_or.value();
    // We don't match two SliceOp with different sizes.
    if (k_values != k_indices && !values.use_empty() && !indices.use_empty())
      return failure();

    // Start replacing.
    auto k = !values.use_empty() ? k_values : k_indices;

to compose drops of Rain they begin to coalesce and constitute Globules of all intermediate sizes, those Globules, when they become of convenient size to reflect some Colours and transmit others, may constitute Clouds of various Colours according to their sizes. And I see not what can be rationally conceived in so transparent a Substance as Water for the production of these Colours, besides the various sizes of its fluid and globular Parcels. PROP. VI. _The parts of Bodies on which their Colours depend,...

        }

        w = new WriterThread();
        w.setDaemon( true );
        w.start();

        /* Downgrade one transport to the lower of the negotiated buffer sizes
         * so we can just send whatever is received.
         */

        SmbTransport t1 = tree.session.transport;
        SmbTransport t2 = dest.tree.session.transport;

  @Test
  fun writeTimeouts() {
    val server = MockWebServer()
    // Sockets on some platforms can have large buffers that mean writes do not block when
    // required. These socket factories explicitly set the buffer sizes on sockets created.
    val socketBufferSize = 4 * 1024
    server.serverSocketFactory =
      object : DelegatingServerSocketFactory(getDefault()) {

           "Maximum shape inference iterations">,
    Option<"input_arg_shapes_", "input-arg-shapes", "std::string", /*default=*/"",
           "Input tensor shapes. Shapes for different tensors are separated by ':', and dimension sizes for the same tensor are separated by ','">,
  ];
}

def StripNoinlineAttributePass : Pass<"tf-strip-noinline-attribute", "ModuleOp"> {
  let summary = "Strip the tf._noinline attribute from top-level functions.";

	case ACMPU:
		return op_CLGFI
	case ACMPW:
		return op_CFI
	case ACMPWU:
		return op_CLFI
	}
	c.ctxt.Diag("unknown ril opcode %v", a)
	return 0
}

// z instructions sizes
const (
	sizeE    = 2
	sizeI    = 2
	sizeIE   = 4
	sizeMII  = 6
	sizeRI   = 4
	sizeRI1  = 4
	sizeRI2  = 4
	sizeRI3  = 4
	sizeRIE  = 6
	sizeRIE1 = 6
	sizeRIE2 = 6

		{name: "BSRL", argLength: 1, reg: gp11, asm: "BSRL", typ: "UInt32", clobberFlags: true}, // # of high-order zeroes in 32-bit arg

		// CMOV instructions: 64, 32 and 16-bit sizes.
		// if arg2 encodes a true result, return arg1, else arg0
		{name: "CMOVQEQ", argLength: 3, reg: gp21, asm: "CMOVQEQ", resultInArg0: true},
		{name: "CMOVQNE", argLength: 3, reg: gp21, asm: "CMOVQNE", resultInArg0: true},

  return lhs.hasRank() && rhs.hasRank() && lhs.getRank() == rhs.getRank();
}

// Creates a compatible RankedTensorType where mismatched dimensions are
// replaced with dynamic sizes.
RankedTensorType GetCompatibleRankedTensorType(RankedTensorType lhs,
                                               RankedTensorType rhs) {
  assert(lhs.getRank() == rhs.getRank());
  llvm::SmallVector<int64_t, 4> dims;

func.func @testPackShapeComputation(%arg0: tensor<?x1xf32>, %arg1: tensor<?x1x2xf32>, %arg2: tensor<*xf32>) -> (tensor<2xi32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>, tensor<*xi32>) {
  // Test dimensions sizes.
  %d1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %d2 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>

  // Slice bounds.