self._create_matmul_model(
        input_shape=(1, 1024),
        weight_shape=(1024, 3),
        saved_model_path=self._input_saved_model_path,
    )

    rng = np.random.default_rng(seed=1234)
    representative_dataset: repr_dataset.RepresentativeDataset = [
        {'input_tensor': rng.uniform(size=(1, 1024)).astype(np.float32)}
        for _ in range(4)
    ]

	1234567890,1234567890,1234567890,1234567890,1234567890,123"`
	// Create 10MiB content where each line contains 1024 characters.
	for i := 0; i < 10*1024; i++ {
		buffer.WriteString(fmt.Sprintf("[%05d] %s\n", i, line))
	}
	putContent := buffer.String()

	buf := bytes.NewReader([]byte(putContent))

	objectName := "test-big-object"

	FSCTL_SET_ZERO_ON_DEALLOCATION            = 0x090194
	FSCTL_SIS_COPYFILE                        = 0x090100
	FSCTL_WRITE_USN_CLOSE_RECORD              = 0x0900EF

	MAXIMUM_REPARSE_DATA_BUFFER_SIZE = 16 * 1024
	IO_REPARSE_TAG_MOUNT_POINT       = 0xA0000003
	IO_REPARSE_TAG_SYMLINK           = 0xA000000C
	SYMBOLIC_LINK_FLAG_DIRECTORY     = 0x1
)

const (
	ComputerNameNetBIOS                   = 0

	cr.unlock()

	cr.cond.Broadcast()
	return n, err
}

var (
	bufioReaderPool   sync.Pool
	bufioWriter2kPool sync.Pool
	bufioWriter4kPool sync.Pool
)

const copyBufPoolSize = 32 * 1024

var copyBufPool = sync.Pool{New: func() any { return new([copyBufPoolSize]byte) }}

func getCopyBuf() []byte {
	return copyBufPool.Get().(*[copyBufPoolSize]byte)[:]
}
func putCopyBuf(b []byte) {