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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def cv_squared(x):
 """The squared coefficient of variation of a sample. Useful as a loss to encourage a positive distribution to be more uniform. Epsilons added for numerical stability. Returns 0 for an empty Tensor. Args: x: a `Tensor`. Returns: a `Scalar`. """

  epsilon = 1e-10
  float_size = tf.to_float(tf.size(x)) + epsilon
  mean = tf.reduce_sum(x) / float_size
  variance = tf.reduce_sum(tf.squared_difference(x, mean)) / float_size
  return variance / (tf.square(mean) + epsilon)