human
Solve the following python programming problem given the following code:
@doctest_depends_on(modules=('matplotlib',))
def plot_implicit(expr, x_var=None, y_var=None, **kwargs):
assistant
#Here is the Python program you requested.

    """A plot function to plot implicit equations / inequalities.
    Arguments
    - ``expr`` : The equation / inequality that is to be plotted.
    - ``x_var`` (optional) : symbol to plot on x-axis or tuple giving symbol
    and range as ``(symbol, xmin, xmax)``
    - ``y_var`` (optional) : symbol to plot on y-axis or tuple giving symbol
    and range as ``(symbol, ymin, ymax)``
    If neither ``x_var`` nor ``y_var`` are given then the free symbols in the
    expression will be assigned in the order they are sorted.
    The following keyword arguments can also be used:
    - ``adaptive``. Boolean. The default value is set to True. It has to be
    set to False if you want to use a mesh grid.
    - ``depth`` integer. The depth of recursion for adaptive mesh grid.
    Default value is 0. Takes value in the range (0, 4).
    - ``points`` integer. The number of points if adaptive mesh grid is not
    used. Default value is 200.
    - ``title`` string .The title for the plot.
    - ``xlabel`` string. The label for the x-axis
    - ``ylabel`` string. The label for the y-axis
    Aesthetics options:
    - ``line_color``: float or string. Specifies the color for the plot.
    See ``Plot`` to see how to set color for the plots.
    plot_implicit, by default, uses interval arithmetic to plot functions. If
    the expression cannot be plotted using interval arithmetic, it defaults to
    a generating a contour using a mesh grid of fixed number of points. By
    setting adaptive to False, you can force plot_implicit to use the mesh
    grid. The mesh grid method can be effective when adaptive plotting using
    interval arithmetic, fails to plot with small line width.
    Examples
    Plot expressions:
    >>> from sympy import plot_implicit, cos, sin, symbols, Eq, And
    >>> x, y = symbols(\x y\)
    Without any ranges for the symbols in the expression
    >>> p1 = plot_implicit(Eq(x**2 + y**2, 5))
    With the range for the symbols
    >>> p2 = plot_implicit(Eq(x**2 + y**2, 3),
    ...         (x, -3, 3), (y, -3, 3))
   ..."""
    has_equality = False
    def arg_expand(bool_expr):
        '\n                                Recursively    expands    the    arguments    of    an    Boolean    Function\n                                '
        for arg in bool_expr.args:
            if isinstance(arg, BooleanFunction):
                arg_expand(arg)
            elif isinstance(arg, Relational):
                arg_list.append(arg)
    arg_list = []
    if isinstance(expr, BooleanFunction):
        arg_expand(expr)
        if any((isinstance(e, (Equality, GreaterThan, LessThan)) for e in arg_list)):
            has_equality = True
    elif (not isinstance(expr, Relational)):
        expr = Eq(expr, 0)
        has_equality = True
    elif isinstance(expr, (Equality, GreaterThan, LessThan)):
        has_equality = True
    xyvar = [i for i in (x_var, y_var) if (i is not None)]
    free_symbols = expr.free_symbols
    range_symbols = Tuple(*flatten(xyvar)).free_symbols
    undeclared = (free_symbols - range_symbols)
    if (len((free_symbols & range_symbols)) > 2):
        raise NotImplementedError('Implicit    plotting    is    not    implemented    for    more    than    2    variables')
    default_range = Tuple((-5), 5)
    def _range_tuple(s):
        if isinstance(s, Symbol):
            return (Tuple(s) + default_range)
        if (len(s) == 3):
            return Tuple(*s)
        raise ValueError(('symbol    or    `(symbol,    min,    max)`    expected    but    got    %s' % s))
    if (len(xyvar) == 0):
        xyvar = list(_sort_gens(free_symbols))
    var_start_end_x = _range_tuple(xyvar[0])
    x = var_start_end_x[0]
    if (len(xyvar) != 2):
        if ((x in undeclared) or (not undeclared)):
            xyvar.append(Dummy(('f(%s)' % x.name)))
        else:
            xyvar.append(undeclared.pop())
    var_start_end_y = _range_tuple(xyvar[1])
    use_interval = kwargs.pop('adaptive', True)
    nb_of_points = kwargs.pop('points', 300)
    depth = kwargs.pop('depth', 0)
    line_color = kwargs.pop('line_color', 'blue')
   ...