def tf_solve(self, fn_x, x_init, *args):
self.fn_x = fn_x<EOL>args = self.initialize(x_init, *args)<EOL>if self.unroll_loop:<EOL><INDENT>for _ in range(self.max_iterations):<EOL><INDENT>next_step = self.next_step(*args)<EOL>step = (lambda: self.step(*args))<EOL>do_nothing = (lambda: args)<EOL>args = tf.cond(pred=next_step, true_fn=step, false_fn=do_nothing)<EOL><DEDENT><DEDENT>else:<EOL><INDENT>args = tf.while_loop(cond=self.next_step, body=self.step, loop_vars=args)<EOL><DEDENT>return args[<NUM_LIT:0>]<EOL>
Iteratively solves an equation/optimization for $x$ involving an expression $f(x)$.

Args:
    fn_x: A callable returning an expression $f(x)$ given $x$.
    x_init: Initial solution guess $x_0$.
    *args: Additional solver-specific arguments.

Returns:
    A solution $x$ to the problem as given by the solver.