Select variable_scope dynamically at runtime

I want to change the variable_scope by the value of some tensors. For an easy example, I defined a very simple code like this:

import tensorflow as tf

def calculate_variable(scope):
    with tf.variable_scope(scope or type(self).__name__, reuse=tf.AUTO_REUSE):
        w = tf.get_variable('ww', shape=[5], initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
        return w

w = calculate_variable('in_first')
w1 = calculate_variable('in_second')

The function is very simple. It just returns value defined in a certain variable scope. Now, 'w' and 'w1' would have different values.

What I want to do is to select this variable scope by some condition of tensors. Assuming I have two tensors x, y, if their value is same, I want to get value from the function above with certain variable scope.

x = tf.constant(3)
y = tf.constant(3)
condi = tf.cond(tf.equal(x, y), lambda: 'in_first', lambda: 'in_second')

w_cond = calculate_variable(condi)

I tried many other methods and searched the Internet. However, whenever I want to determine variable_scope from condition of tensors in a similar way to this example, it shows an error.

TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.

Is there any good workaround?