human
I am working on the file "1_foundations/5_nqueens/constraint_satisfaction.ipynb".
The first task is:
The N-Queens CSP Class
Implement the CSP class as described above, with constraints to make sure each queen is on a different row and different diagonal than every other queen, and a variable for each column defining the row that containing a queen in that column.
Can you write Python program for it?

=====
Write me a the Python program you requested?
assistant
#Here is the Python program you requested.

class NQueensCSP:
    """CSP representation of the N-queens problem
    
    Parameters
    ----------
    N : Integer
        The side length of a square chess board to use for the problem, and
        the number of queens that must be placed on the board
    """
    def __init__(self, N):
        _vars = symbols(f'A0:{N}')
        _domain = set(range(N))
        self.size = N
        self.variables = _vars
        self.domains = {v: _domain for v in _vars}
        self._constraints = {x: set() for x in _vars}

        # add constraints - for each pair of variables xi and xj, create
        # a diffRow(xi, xj) and a diffDiag(xi, xj) instance, and add them
        # to the self._constraints dictionary keyed to both xi and xj;
        # (i.e., add them to both self._constraints[xi] and self._constraints[xj])
        for i in range(N):
            for j in range(i + 1, N):
                diffRowConstraint = diffRow.subs({r1: _vars[i], r2: _vars[j]})
                diffDiagConstraint = diffDiag.subs({r1: _vars[i], r2: _vars[j], c1:i, c2:j})
                self._constraints[_vars[i]].add(diffRowConstraint)
                self._constraints[_vars[i]].add(diffDiagConstraint)
                self._constraints[_vars[j]].add(diffRowConstraint)
                self._constraints[_vars[j]].add(diffDiagConstraint)
    
    @property
    def constraints(self):
        """Read-only list of constraints -- cannot be used for evaluation """
        constraints = set()
        for _cons in self._constraints.values():
            constraints |= _cons
        return list(constraints)
    
    def is_complete(self, assignment):
        """An assignment is complete if it is consistent, and all constraints
        are satisfied.
        
        Hint: Backtracking search checks consistency of each assignment, so checking
        for completeness can be done very efficiently
        
        Parameters
        ----------
        assignment : dict(sympy.Symbol: Integer)
            An assignment of values to variables that have...