code: def implied_volatility_from_raw_svi_parameters(
      *,
      svi_parameters: types.RealTensor,
      log_moneyness: types.RealTensor = None,
      forwards: types.RealTensor = None,
      strikes: types.RealTensor = None,
      expiries: types.RealTensor = None,
      dtype: tf.DType = None,
      name: str = None,
  ) -> types.RealTensor:
      r"""Computes modeled implied volatility using raw SVI parameters.
  
      The SVI volatility model parameterizes an option's total implied variance. For
      a fixed timeslice (i.e. given expiry t), raw SVI parameters (a,b,rho,m,sigma)
      and option's log-moneyness k:=log(K/F), the modeled total variance is
      ```None
      w(k) = a + b * (rho * (k - m) + sqrt{(k - m)^2 + sigma^2)}
      ```
  
      The modeled Black-Scholes implied volatility sigmaBS is computed from w(k)
      and the option's expiry t from the equation
      ```None
      w(k,t) = sigmaBS(k,t)^2 * t
      ```
  
      See [1] and documentation for `total_variance_from_raw_svi_parameters` for
      additional details.
  
      #### Example
  
      ```python
      import numpy as np
      import tensorflow.compat.v2 as tf
      import tf_quant_finance as tff
  
      svi_parameters = np.array([-0.1825, 0.3306, -0.0988, 0.0368, 0.6011])
  
      forwards = np.array([2402.])
      expiries = np.array([0.23])
      strikes = np.array([[1800., 2000., 2200., 2400., 2600., 2800., 3000.]])
  
      implied_vol = tff.experimental.svi.implied_volatility_from_raw_svi_parameters(
          svi_parameters=svi_parameters,
          forwards=forwards,
          strikes=strikes,
          expiries=expiries)
  
      # Expected: implied_vol tensor (rounded to 4 decimal places) should contain
      # [[0.4849, 0.3972, 0.3265, 0.2785, 0.2582, 0.2647, 0.2905]]
      ```
  
      #### References:
      [1] Gatheral J., Jaquier A., Arbitrage-free SVI volatility surfaces.
      https://arxiv.org/pdf/1204.0646.pdf
  
      Args:
        svi_parameters: A rank 2 real `Tensor` of shape [batch_size, 5]. The raw SVI
          parameters for each volatility skew.
        log_moneyness: A rank 2 real `Tensor` of shape [batch_size, num_strikes].
          The log-moneyness of the options.
        forwards: A rank 2 real `Tensor` of shape [batch_size, num_strikes]. The
          forward prices of the options at expiries.
        strikes: A rank 2 real `Tensor` of shape [batch_size, num_strikes]. The
          options strike prices.
        expiries: A rank 1 real `Tensor` of shape [batch_size]. The options
          expiries.
        dtype: Optional `tf.Dtype`. If supplied, the dtype for the input and output
          `Tensor`s will be converted to this.
          Default value: `None` which maps to the dtype inferred from
            `log_moneyness`.
        name: Python str. The name to give to the ops created by this function.
          Default value: `None` which maps to `svi_implied_volatility`.
  
      Returns:
        A rank 2 real `Tensor` of shape [batch_size, num_strikes].
  
      Raises:
        ValueError: If exactly one of `forwards` and `strikes` is supplied.
        ValueError: If both `log_moneyness' and `forwards` are supplied or if
        neither is supplied.
      """
      name = name or "svi_implied_volatility"
      with tf.name_scope(name):
          total_variance = total_variance_from_raw_svi_parameters(
              svi_parameters=svi_parameters,
              log_moneyness=log_moneyness,
              forwards=forwards,
              strikes=strikes,
              name=name,
          )
          dtype = dtype or total_variance.dtype
          expiries = tf.convert_to_tensor(
              expiries, dtype, name="expiries"
          )
          implied_volatilities = tf.math.sqrt(
              total_variance / expiries[:, None]
          )
          return implied_volatilities
  
imports: import tensorflow.compat.v2
  from tf_quant_finance import types
label_desc: Use image node_id 2 for calling a global function with example usage: implied_volatility_from_raw_svi_parameters() and returns: implied_volatilities