code: def _zeta_by_xhat(zeta, rho, dtype):
      zbxh = tf.math.divide_no_nan(
          zeta,
          tf.math.log(
              (
                  tf.math.sqrt(1 - 2 * rho * zeta + zeta * zeta)
                  - rho
                  + zeta
              )
              / (1.0 - rho)
          ),
      )
      eps = _epsilon(dtype)
  
      # When zeta -> 0, the limit of zeta / x_hat(zeta) reduces to 1.0
      return tf.where(tf.abs(zeta) > eps, zbxh, 1.0)
  
file_path: tf-quant-finance/tf_quant_finance/models/sabr/approximations/implied_volatility.py
imports: import enum
  import tensorflow.compat.v2
label_desc: Use image node_id 3 for calling a global function with example usage: _zeta_by_xhat(zeta, rho, dtype) and returns: tf