// 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
}

// -----

// CHECK: return %[[SUB_0]] : tensor<15x28x28x1xf32>
}

// CHECK-LABEL: depth_to_space
func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> {
  %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64,  data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
  func.return %0 : tensor<1x2x2x1xf32>

    %1 = "tf.IteratorGetNext"(%arg5) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<!tf_type.resource>) -> tensor<2x224x224x3xf32>
    // CHECK-DAG: %[[SPACETODEPTH0:.*]] = "tf.SpaceToDepth"([[INPUT:.*]]) <{block_size = 2 : i64, data_format = "NHWC"}> : (tensor<2x224x224x3xf32>) -> tensor<2x112x112x12xf32>
    %2 = "tf.AddV2"(%arg2, %arg3) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

table BatchToSpaceNDOptions {
}

table SkipGramOptions {
  ngram_size: int;
  max_skip_size: int;
  include_all_ngrams: bool;
}

table SpaceToDepthOptions {
  block_size: int;
}

table DepthToSpaceOptions {
  block_size: int;
}

table SubOptions {
  fused_activation_function:ActivationFunctionType;
}

table DivOptions {
  fused_activation_function:ActivationFunctionType;
}

// CHECK: return %[[SUB_0]] : tensor<15x28x28x1xf32>
}

// CHECK-LABEL: depth_to_space
func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> {
  %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64,  data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
  func.return %0 : tensor<1x2x2x1xf32>

	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.

table BatchToSpaceNDOptions {
}

table SkipGramOptions {
  ngram_size: int;
  max_skip_size: int;
  include_all_ngrams: bool;
}

table SpaceToDepthOptions {
  block_size: int;
}

table DepthToSpaceOptions {
  block_size: int;
}

table SubOptions {
  fused_activation_function:ActivationFunctionType;
  // Parameters supported by version 5
  pot_scale_int16:bool = true;
}

The attr `block_size` indicates the input block size and how the data is moved.

  * Chunks of data of size `block_size * block_size` from depth are rearranged
    into non-overlapping blocks of size `block_size x block_size`
  * The width of the output tensor is `input_depth * block_size`, whereas the
    height is `input_height * block_size`.

}

table SkipGramOptions {
  ngram_size: int;
  max_skip_size: int;
  include_all_ngrams: bool;
}

table SpaceToDepthOptions {
  block_size: int;
}

table DepthToSpaceOptions {
  block_size: int;
}

table SubOptions {
  fused_activation_function:ActivationFunctionType;
  // Parameters supported by version 5
  pot_scale_int16:bool = true;
}

	// on systems with coarse timer granularity.
	const blockTime = 100 * time.Millisecond

	// Make sure the goroutine spawned above actually blocks on the lock.
	for {
		if runtime.GIsWaitingOnMutex(gp) {
			break
		}
		runtime.Gosched()
	}

	// Let some amount of time pass.
	time.Sleep(blockTime)

	// Let the other goroutine acquire the lock.
	mu.Unlock1()
	done <- true