//  [python]
def __repr__(self):
        """
        Literal string representation.

        """
        if self._baseclass is np.ndarray:
            name = 'array'
        else:
            name = self._baseclass.__name__


        # 2016-11-19: Demoted to legacy format
        if np._core.arrayprint._get_legacy_print_mode() <= 113:
            is_long = self.ndim > 1
            parameters = dict(
                name=name,
                nlen=" " * len(name),
                data=str(self),
                mask=str(self._mask),
                fill=str(self.fill_value),
                dtype=str(self.dtype)
            )
            is_structured = bool(self.dtype.names)
            key = '{}_{}'.format(
                'long' if is_long else 'short',
                'flx' if is_structured else 'std'
            )
            return _legacy_print_templates[key] % parameters

        prefix = f"masked_{name}("

        dtype_needed = (
            not np._core.arrayprint.dtype_is_implied(self.dtype) or
            n.all(self.mask) or
            self.size == 0
        )

        # determine which keyword args need to be shown
        keys = ['data', 'mask', 'fill_value']
        if dtype_needed:
            keys.append('dtype')

        # array has only one row (non-column)
        is_one_row = builtins.all(dim == 1 for dim in self.shape[:-1])

        # choose what to indent each keyword with
        min_indent = 2
        if is_one_row:
            # first key on the same line as the type, remaining keys
            # aligned by equals
            indents = {}
            indents[keys[0]] = prefix
            for k in keys[1:]:
                n = builtins.max(min_indent, len(prefix + keys[0]) - len(k))
                indents[k] = ' ' * n
            prefix = ''  # absorbed into the first indent
        else:
            # each key on its own line, indented by two spaces
            indents = {k: ' ' * min_indent for k in keys}
            prefix = prefix + '\n'  # first key on the next line

        # format the field values
        reprs = {}
        reprs['data'] = np.array2string(
            self._insert_masked_print(),
            separator=", ",
            prefix=indents['data'] + 'data=',
            suffix=',')
        reprs['mask'] = np.array2string(
            self._mask,
            separator=", ",
            prefix=indents['mask'] + 'mask=',
            suffix=',')

        if self._fill_value is None:
            self.fill_value  # initialize fill_value

        if (self._fill_value.dtype.kind in ("S", "U")
                and self.dtype.kind == self._fill_value.dtype.kind):
            # Allow strings: "N/A" has length 3 so would mismatch.
            fill_repr = repr(self.fill_value.item())
        elif self._fill_value.dtype == self.dtype and not self.dtype == object:
            # Guess that it is OK to use the string as item repr.  To really
            # fix this, it needs new logic (shared with structured scalars)
            fill_repr = str(self.fill_value)
        else:
            fill_repr = repr(self.fill_value)

        reprs['fill_value'] = fill_repr
        if dtype_needed:
            reprs['dtype'] = np._core.arrayprint.dtype_short_repr(self.dtype)

        # join keys with values and indentations
        result = ',\n'.join(
            '{}{}={}'.format(indents[k], k, reprs[k])
            for k in keys
        )
        return prefix + result + ')'