How to use the numcodecs.compat.ensure_ndarray function in numcodecs

def decode(self, buf, out=None):

        # interpret buffer as numpy array
        enc = ensure_ndarray(buf).view(self.astype)

        # flatten to simplify implementation
        enc = enc.reshape(-1, order='A')

        # decode scale offset
        dec = (enc / self.scale) + self.offset

        # convert dtype
        dec = dec.astype(self.dtype, copy=False)

        # handle output
        return ndarray_copy(dec, out)

def decode(self, buf, out=None):

        # normalise input
        enc = ensure_ndarray(buf).view(self.astype)

        # flatten to simplify implementation
        enc = enc.reshape(-1, order='A')

        # setup output
        dec = np.full_like(enc, fill_value='', dtype=self.dtype)

        # apply decoding
        for i, l in enumerate(self.labels):
            dec[enc == (i + 1)] = l

        # handle output
        dec = ndarray_copy(dec, out)

        return dec

def encode(self, buf):

        # normalise input
        arr = ensure_ndarray(buf).view(self.dtype)

        # flatten to simplify implementation
        arr = arr.reshape(-1, order='A')

        # setup encoded output
        enc = np.empty_like(arr, dtype=self.astype)

        # set first element
        enc[0] = arr[0]

        # compute differences
        enc[1:] = np.diff(arr)

        return enc

def decode(self, buf, out=None):

        # normalise input
        enc = ensure_ndarray(buf).view(self.astype)

        # flatten to simplify implementation
        enc = enc.reshape(-1, order='A')

        # setup decoded output
        dec = np.empty_like(enc, dtype=self.dtype)

        # decode differences
        np.cumsum(enc, out=dec)

        # handle output
        out = ndarray_copy(dec, out)

        return out

def encode(self, buf):

        # normalise input
        arr = ensure_ndarray(buf).view(self.decode_dtype)

        # convert and copy
        enc = arr.astype(self.encode_dtype)

        return enc

def encode(self, buf):

        # normalise input
        arr = ensure_ndarray(buf).view(self.dtype)

        # apply scaling
        precision = 10. ** -self.digits
        exp = math.log(precision, 10)
        if exp < 0:
            exp = int(math.floor(exp))
        else:
            exp = int(math.ceil(exp))
        bits = math.ceil(math.log(10. ** -exp, 2))
        scale = 2. ** bits
        enc = np.around(scale * arr) / scale

        # cast dtype
        enc = enc.astype(self.astype, copy=False)

        return enc

def encode(self, buf):

        # normalise input
        if self.dtype == object:
            arr = np.asarray(buf, dtype=object)
        else:
            arr = ensure_ndarray(buf).view(self.dtype)

        # flatten to simplify implementation
        arr = arr.reshape(-1, order='A')

        # setup output array
        enc = np.zeros_like(arr, dtype=self.astype)

        # apply encoding, reserving 0 for values not specified in labels
        for i, l in enumerate(self.labels):
            enc[arr == l] = i + 1

        return enc

def encode(self, buf):

        # normalise input
        arr = ensure_ndarray(buf).view(self.dtype)

        # flatten to simplify implementation
        arr = arr.reshape(-1, order='A')

        # compute scale offset
        enc = (arr - self.offset) * self.scale

        # round to nearest integer
        enc = np.around(enc)

        # convert dtype
        enc = enc.astype(self.astype, copy=False)

        return enc

def decode(self, buf, out=None):
        # filter is lossy, decoding is no-op
        dec = ensure_ndarray(buf).view(self.astype)
        dec = dec.astype(self.dtype, copy=False)
        return ndarray_copy(dec, out)

def decode(self, buf, out=None):

        # normalise input
        enc = ensure_ndarray(buf).view('u1')

        # flatten to simplify implementation
        enc = enc.reshape(-1, order='A')

        # find out how many bits were padded
        n_bits_padded = int(enc[0])

        # apply decoding
        dec = np.unpackbits(enc[1:])

        # remove padded bits
        if n_bits_padded:
            dec = dec[:-n_bits_padded]

        # view as boolean array
        dec = dec.view(bool)