code: def _unconstrained_to_raw_svi(unconstrained_parameters):
      """Converts unconstrained optimizarion parameters to raw SVI ones.
  
      Performs the inverse transformation of the internal unconstrained model
      parameters into the standard raw SVI parameters `a, b, rho, m, sigma`.
  
      Args:
        unconstrained_parameters: A rank 2 real `Tensor` of shape [batch_size, 5],
          representing SVI model's raw parameters.
  
      Returns:
        A rank 2 real `Tensor` of shape [batch_size, 5], representing the
        unconstrained parameters, used in internal optimization of the SVI model.
      """
      b = tf.math.exp(unconstrained_parameters[..., 1])
      rho = 2 * tf.math.sigmoid(unconstrained_parameters[..., 2]) - 1
      m = unconstrained_parameters[..., 3]
      sigma = tf.math.exp(unconstrained_parameters[..., 4])
      a = tf.math.exp(
          unconstrained_parameters[..., 0]
      ) - b * sigma * tf.math.sqrt(1 - rho**2)
      # Return shape: `[batch_size, 5]`
      return tf.transpose([a, b, rho, m, sigma])
  
file_path: tf-quant-finance/tf_quant_finance/experimental/svi/calibration.py
imports: from typing import Callable, Tuple
  import tensorflow.compat.v2
  from tf_quant_finance import types
  from tf_quant_finance.experimental.svi import parameterizations
  from tf_quant_finance.math import make_val_and_grad_fn
  from tf_quant_finance.math import optimizer
label_desc: Use image node_id 4 for calling a global function with example usage: _unconstrained_to_raw_svi(unconstrained_parameters) and returns: tf