How to use the h5pyd._hl.base.guess_dtype function in h5pyd

(Scalar) Use this value for uninitialized parts of the dataset.
        track_times
            (T/F) Enable dataset creation timestamps.
        """


        if self.id.http_conn.mode == 'r':
            raise ValueError("Unable to create dataset (No write intent on file)")

        if shape is None and data is None:
            raise TypeError("Either data or shape must be specified")

        # Convert data to a C-contiguous ndarray
        if data is not None:
            if dtype is None:
                dtype = guess_dtype(data)
            # if dtype is None:
            #     dtype = numpy.float32
            if not isinstance(data, numpy.ndarray) or dtype != data.dtype:
                data = numpy.asarray(data, order="C", dtype=dtype)
                dtype = data.dtype
            self.log.info("data dtype: {}".format(data.dtype))

        # Validate shape
        if shape is None:
            if data is None:
                raise TypeError("Either data or shape must be specified")
            shape = data.shape
        else:
            shape = tuple(shape)
        if data is not None and (numpy.product(shape) != numpy.product(data.shape)):
            raise ValueError("Shape tuple is incompatible with data")

def __setitem__(self, name, value):
        """ Set a new attribute, overwriting any existing attribute.

        The type and shape of the attribute are determined from the data.  To
        use a specific type or shape, or to preserve the type of an attribute,
        use the methods create() and modify().
        """
        self.create(name, data=value, dtype=base.guess_dtype(value))

isn't provided.
        dtype
            Numpy dtype or string.  If omitted, dtype('f') will be used.
            Required if "data" isn't provided; otherwise, overrides data
            array's dtype.
        data
            Provide data to initialize the dataset.  If used, you can omit
            shape and dtype arguments.

        Keyword-only arguments:
        """
        # Convert data to a C-contiguous ndarray
        shape = None
        if data is not None:
            if dtype is None:
                dtype = guess_dtype(data)
            if dtype is None:
                dtype = numpy.float32
            if not isinstance(data, numpy.ndarray) or dtype != data.dtype:
                data = numpy.asarray(data, order="C", dtype=dtype)
            self.log.info("data dtype: {}".format(data.dtype))
            if len(data.shape) != 1:
                ValueError("Table must be one-dimensional")
            if numrows and numrows != data.shape[0]:
                ValueError("Data does not match numrows value")
            shape = data.shape
        elif numrows:
            shape = [numrows,]
        else:
            shape = [0,]

        if dtype is None: