int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),
				testCase.accessKey, testCase.secretKey)
		case testCase.contentEncoding == "":
			req, err = newTestStreamingSignedRequest(http.MethodPut,
				getPutObjectURL("", testCase.bucketName, testCase.objectName),
				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),

    """Finds the optimal quant_min and quant_max by testing all possible cases.

    It guarantees optimal quant_min and quant_max for the representative
    dataset, but not for the test dataset.

    Returns:
      (min_value, max_value): Min and max calculated using
      HistogramMseBruteforce.
    """
    if self._num_bins > 512:
      logging.warning(

// * StaticRangePtqPreset
//   - The preset's `representative_datasets` field will be transferred to
//   `QuantizationConfig.calibration_options.representative_datasets`, unless
//   the user explicitly provided representative dataset configs to
//   `calibration_options`. In that case, the explicit configs take precedence
//   and the preset's configs are ignored.
//   - For `QuantizationSpecs`, the expanded `QuantizationSpec`s will be

      argument.

      `representative_dataset_file_map_serialized` is a signature key ->
      `RepresentativeDatasetFile` (serialized) mapping for running the
      calibration step. Each dataset file stores the representative dataset for
      the function matching the signature key.

      Raises `StatusNotOk` exception if when the run was unsuccessful.
      )pbdoc",

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.

	hdr, err := x.ReadData(addr, uint64(2*ptrSize))
	if err != nil || len(hdr) < 2*ptrSize {
		return ""
	}
	dataAddr := readPtr(hdr)
	dataLen := readPtr(hdr[ptrSize:])
	data, err := x.ReadData(dataAddr, dataLen)
	if err != nil || uint64(len(data)) < dataLen {
		return ""
	}
	return string(data)
}

// elfExe is the ELF implementation of the exe interface.
type elfExe struct {
	f *elf.File
}

    config: Quantization configuration.

  Raises:
    ValueError: When `config` was not configured for static-range PTQ
    single representative dataset.
  """
  # Updates user-provided `QuantizationConfig`s for the internal quantization
  # pipeline to work with.
  print('=== User-provided QuantizationConfig ===')
  print(config)

}

type xmmreg struct {
	element [4]uint32
}

type fpstate struct {
	cw        uint32
	sw        uint32
	tag       uint32
	ipoff     uint32
	cssel     uint32
	dataoff   uint32
	datasel   uint32
	_st       [8]fpreg
	status    uint16
	magic     uint16
	_fxsr_env [6]uint32
	mxcsr     uint32
	reserved  uint32
	_fxsr_st  [8]fpxreg
	_xmm      [8]xmmreg
	padding1  [44]uint32

// pass the operands to.
bool IsSupportedTFLiteControlFlow(Operation* op) {
  return llvm::isa<TFL::WhileOp, TFL::IfOp, TFL::CallOnceOp>(op);
}

// Returns true if the 'op' is one of the supported TF control flow ops or
// dataset ops. Those ops just forward the operands to other subgraphs.
bool IsSupportedTFDataForwardingOp(Operation* op) {
  return llvm::isa<TF::MapDatasetOp, TF::ReduceDatasetOp, TF::CacheDatasetV2Op,

	Len   uint32
	Insns *BpfInsn
}

type BpfInsn struct {
	Code uint16
	Jt   uint8
	Jf   uint8
	K    uint32
}

type BpfHdr struct {
	Tstamp  BpfTimeval
	Caplen  uint32
	Datalen uint32
	Hdrlen  uint16
	Ifidx   uint16
	Flowid  uint16
	Flags   uint8
	Drops   uint8
}

type BpfTimeval struct {
	Sec  uint32
	Usec uint32
}

const (
	_AT_FDCWD = -0x64
)