globalNotificationSys.ServiceFreeze(ctx, false)
	}()

	serial := r.Form.Get("serial") == "true"
	blockSizeStr := r.Form.Get("blocksize")
	fileSizeStr := r.Form.Get("filesize")

	blockSize, err := strconv.ParseUint(blockSizeStr, 10, 64)
	if err != nil {
		blockSize = 4 * humanize.MiByte // default value
	}

	fileSize, err := strconv.ParseUint(fileSizeStr, 10, 64)
	if err != nil {

	if got := c.Size(); got != Size256 {
		t.Errorf("New512256.Size = %d; want %d", got, Size256)
	}
}

func TestBlockSize(t *testing.T) {
	c := New()
	if got := c.BlockSize(); got != BlockSize {
		t.Errorf("BlockSize = %d; want %d", got, BlockSize)
	}
}

// Tests that blockGeneric (pure Go) and block (in assembly for some architectures) match.
func TestBlockGeneric(t *testing.T) {
	if boring.Enabled {

	writeBlocks(ctxt, out, ctxt.outSem, ctxt.loader, ctxt.Textp, addr, size, pad)
}

const blockSize = 1 << 20 // 1MB chunks written at a time.

// writeBlocks writes a specified chunk of symbols to the output buffer. It
// breaks the write up into ≥blockSize chunks to write them out, and schedules
// as many goroutines as necessary to accomplish this task. This call then

    if (input_batch != ShapedType::kDynamic &&
        input_batch % (block_size * block_size) != 0) {
      return op.emitOpError()
             << "requires input batch (dimension 0) to be evenly divisible "
                "by (block_size * block_size), but got input batch "
             << input_batch << " and block_size " << block_size;
    }

    input_shape.assign(input_type.getShape().begin(),

  // expected-error @+1 {{'tf.BatchToSpace' op requires output batch (dimension 0) to be equal to input batch (dimension 0) / (block_size * block_size), but got output batch 8, input batch 16, and block_size 2}}
  %0 = "tf.BatchToSpace"(%arg0, %arg1) {block_size = 2 : i64} : (tensor<16x8x8x3xf32>, tensor<*xi32>) -> tensor<8x8x8x3xf32>
  func.return
}

// -----

	polar Point                   // polar and Point denote different types
)

type TreeNode struct {
	left, right *TreeNode
	value *Comparable
}

type Block interface {
	BlockSize() int
	Encrypt(src, dst []byte)
	Decrypt(src, dst []byte)
}
</pre>

<p>
A defined type may have <a href="#Method_declarations">methods</a> associated with it.

func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> {
  // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32>
  // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  func.return %0 : tensor<1x1x1x4xf32>
}

// -----

func.func @space_to_depth(%arg0: tensor<1x2x2x1xf32>) -> tensor<?xf32> {
  %0 = "tf.SpaceToDepth"(%arg0) {block_size = 2: i64,  data_format = "NHWC"}: (tensor<1x2x2x1xf32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>

  // CHECK-LABEL: space_to_depth
  // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32>
  // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<?xf32>
}

    and `width` dimensions are moved to the `depth` dimension.
    `block_size` indicates the input block size.
   }];

  let arguments = (ins
    TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8]>:$input,
    ConfinedAttr<I32Attr, [IntPositive]>:$block_size
  );

  let results = (outs
    TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8]>:$output
  );

  %0 = "tf.BatchToSpace"(%arg0, %arg1) {block_size = 8 : i64} : (tensor<*xf32>, tensor<?x?xi32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-LABEL: testBatchToSpaceDynamicCrops
func.func @testBatchToSpaceDynamicCrops(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<*xi32>) -> tensor<*xf32> {
  // CHECK-NOT: "tf.BatchToSpaceND"