`paddings` you specify. `paddings` must be the same as `paddings` argument
given to the corresponding `MirrorPad` op.

The folded size of each dimension D of the output is:

`input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`

For example:

```
# 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
# 'paddings' is [[0, 1]], [0, 1]].
# 'mode' is SYMMETRIC.

    ArrayAttr window_strides, StringRef padding, Builder *builder) {
  if (padding == "VALID") return {};
  assert(padding == "SAME");
  PaddingArray paddings = GetReduceWindowPaddingAsArray<num_dims>(
      input_dims, window_dims, window_strides, padding, builder);
  int64_t rank = paddings.size();
  llvm::SmallVector<int64_t, num_dims * 2> flatten_paddings(rank * 2);

    if (!window_strides.empty()) {
      dim->set_stride(window_strides[i]);
    } else {
      dim->set_stride(1);
    }
    if (!padding.empty()) {
      dim->set_padding_low(padding[i].first);
      dim->set_padding_high(padding[i].second);
    } else {
      dim->set_padding_low(0);
      dim->set_padding_high(0);
    }
    if (!lhs_dilation.empty()) {

  pool_params.filter_height = op.filter_h().getSExtValue();
  pool_params.filter_width = op.filter_w().getSExtValue();
  const auto padding = GetTflitePadding(inst, op.padding());
  if (padding) {
    pool_params.padding = *padding;
    pool_params.activation = kTfLiteActNone;
    pool_params.computed.padding = TfLitePaddingValues{0, 0, 0, 0};
    return pool_params;
  }

  return std::nullopt;
}

namespace {

			pidx++
			continue
		}
		if loggingEnabled {
			state.logf("param %v:\n  [<entry>, %d]:\n", n, afterPrologVal)
		}
		rtypes, _ := inp.RegisterTypesAndOffsets()
		padding := make([]uint64, 0, 32)
		padding = inp.ComputePadding(padding)
		for k, r := range inp.Registers {
			var reg int16
			if n == f.CloSlot {
				reg = cloReg
			} else {
				reg = ObjRegForAbiReg(r, f.Config)
			}

// For example "15:04:05,000" or "15:04:05.000" formats or parses with
// millisecond precision.
//
// Some valid layouts are invalid time values for time.Parse, due to formats
// such as _ for space padding and Z for zone information.
const (
	Layout      = "01/02 03:04:05PM '06 -0700" // The reference time, in numerical order.
	ANSIC       = "Mon Jan _2 15:04:05 2006"
	UnixDate    = "Mon Jan _2 15:04:05 MST 2006"

	SSEIVSize = 32 // 32 bytes

	// SSEDAREPackageBlockSize - SSE dare package block size.
	SSEDAREPackageBlockSize = 64 * 1024 // 64KiB bytes

	// SSEDAREPackageMetaSize - SSE dare package meta padding bytes.
	SSEDAREPackageMetaSize = 32 // 32 bytes

)

// KMSKeyID returns in AWS compatible KMS KeyID() format.
func (o *ObjectInfo) KMSKeyID() string { return kmsKeyIDFromMetadata(o.UserDefined) }

func newS2CompressReader(r io.Reader, on int64, encrypted bool) (rc io.ReadCloser, idx func() []byte) {
	pr, pw := io.Pipe()
	// Copy input to compressor
	opts := compressOpts
	if encrypted {
		// The values used for padding are not a security concern,
		// but we choose pseudo-random numbers instead of just zeros.
		rng := rand.New(rand.NewSource(time.Now().UnixNano()))

	case size == 0:
		buffer = make([]byte, 1) // Allocate at least a byte to reach EOF
	case size == -1:
		if size := data.ActualSize(); size > 0 && size < fi.Erasure.BlockSize {
			// Account for padding and forced compression overhead and encryption.
			buffer = make([]byte, data.ActualSize()+256+32+32, data.ActualSize()*2+512)
		} else {
			buffer = globalBytePoolCap.Load().Get()

            layer. These padding masks will be combined with any
            `attention_mask` passed in directly when calling the layer. This can
            be used with
            [`tf.keras.layers.Embedding`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)
            with `mask_zero=True` to automatically infer a correct padding mask.