for _ in range(num_samples)
    ]

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for sample in samples:
        yield {'input_tensor': ops.convert_to_tensor(sample)}

    with self.session() as sess:
      new_repr_ds = repr_dataset.replace_tensors_by_numpy_ndarrays(
          data_gen(), sess
      )

      # The resulting dataset should not contain any tf.Tensors.

    ).astype(np.float32)

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(50):
        yield {'input_tensor': random_tensor_gen_fn()}

    dataset_path = self.create_tempfile('tfrecord').full_path
    path_map = {'serving_default': dataset_path}
    repr_dataset.TfRecordRepresentativeDatasetSaver(path_map).save(
        {'serving_default': data_gen()}
    )

        op_set=quant_opts_pb2.TF,
    )

    data_gen = self._create_data_generator(
        input_key='x', shape=input_placeholder.shape
    )

    converted_model = quantize_model.quantize(
        self._input_saved_model_path,
        self._output_saved_model_path,
        quantization_options,
        representative_dataset=data_gen,
    )

    self.assertIsNotNone(converted_model)

      dtype: The type of the generated input - usually dtypes.float32 for float
        and dtypes.int64 for int
      num_examples: Number of examples in the representative dataset.

    Yields:
      data_gen: A `quantize_model._RepresentativeSample` filled with random
        values.
    """
    for _ in range(num_examples):
      yield {input_key: random_ops.random_uniform(shape, minval, maxval, dtype)}

func (w *window) appendTo(buf []byte, from, to uint32) []byte {
	dataLen := uint32(len(w.data))
	from += uint32(w.off)
	to += uint32(w.off)

	wrap := false
	if from > dataLen {
		from -= dataLen
		wrap = !wrap
	}
	if to > dataLen {
		to -= dataLen
		wrap = !wrap
	}

	if wrap {
		buf = append(buf, w.data[from:]...)
		return append(buf, w.data[:to]...)

// structure, taking into account any necessary alignment.
func CmsgLen(datalen int) int {
	return cmsgAlignOf(SizeofCmsghdr) + datalen
}

// CmsgSpace returns the number of bytes an ancillary element with
// payload of the passed data length occupies.
func CmsgSpace(datalen int) int {
	return cmsgAlignOf(SizeofCmsghdr) + cmsgAlignOf(datalen)
}

func (h *Cmsghdr) data(offset uintptr) unsafe.Pointer {

			data = data[1:]
			offset++
		}

		for len(data) > 0 {
			dataLen := int32(len(data))
			var segmentEnd, zeroEnd int32
			if len(segments)+(len(dataSects)-secIndex) == maxNumSegments {
				segmentEnd = dataLen
				zeroEnd = dataLen
			} else {
				for {
					// look for beginning of zeroes
					for segmentEnd < dataLen && data[segmentEnd] != 0 {
						segmentEnd++
					}

				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),

	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
}

	WeakBindOff, WeakBindLen uint32
	LazyBindOff, LazyBindLen uint32
	ExportOff, ExportLen     uint32
}

type linkEditDataCmd struct {
	Cmd              macho.LoadCmd
	Len              uint32
	DataOff, DataLen uint32
}

type encryptionInfoCmd struct {
	Cmd                macho.LoadCmd
	Len                uint32
	CryptOff, CryptLen uint32
	CryptId            uint32
}

type uuidCmd struct {
	Cmd  macho.LoadCmd