class_docstr: Represents a batch of Caps and/or Floors.
  
  An interest Cap (or Floor) is a portfolio of call (or put) options where the
  underlying for the individual options are successive forward rates. The
  individual options comprising a Cap are called Caplets and the corresponding
  options comprising a Floor are called Floorlets. For example, a
  caplet on forward rate `F(T_i, T_{i+1})` has the following payoff at time
  `T_{i_1}`:
  
  caplet payoff = tau_i * max[F(T_i, T_{i+1}), 0]
  
  where `tau_i` is the daycount fraction.
  
  The CapAndFloor class can be used to create and price multiple Caps/Floors
  simultaneously. However all instruments within an object must be priced using
  common reference/discount curve.
  
  ### Example:
  The following example illustrates the construction of an IRS instrument and
  calculating its price.
  
  ```python
  import numpy as np
  import tensorflow as tf
  import tf_quant_finance as tff
  dates = tff.datetime
  instruments = tff.experimental.instruments
  rc = tff.experimental.instruments.rates_common
  
  dtype = np.float64
  notional = 100.0
  maturity_date = dates.convert_to_date_tensor([(2022, 1, 15)])
  start_date = dates.convert_to_date_tensor([(2021, 1, 15)])
  valuation_date = dates.convert_to_date_tensor([(2021, 1, 1)])
  
  period3m = dates.periods.months(3)
  cap = instruments.CapAndFloor(
      start_date,
      maturity_date,
      period3m,
      0.005,
      daycount_convention=instruments.DayCountConvention.ACTUAL_365,
      notional=notional,
      dtype=dtype)
  curve_dates = valuation_date + dates.periods.months([0, 3, 12, 24])
  reference_curve = instruments.RateCurve(
      curve_dates,
      np.array([0.005, 0.01, 0.015, 0.02], dtype=np.float64),
      valuation_date=valuation_date,
      dtype=np.float64)
  market = instruments.InterestRateMarket(
      reference_curve=reference_curve, discount_curve=reference_curve)
  
  price = cap.price(
            valuation_date,
            market,
            model=instruments.InterestRateModelType.LOGNORMAL_RATE,
            pricing_context=0.5)
  # Expected result: 1.0474063612452953
  ```
  
  ### References:
  [1]: Leif B.G. Andersen and Vladimir V. Piterbarg. Interest Rate Modeling,
      Volume I: Foundations and Vanilla Models. Chapter 5. 2010.
docstr: Returns the relevant forward rates from the market data.
code: def _get_forward_rate(self, valuation_date, market):
      """Returns the relevant forward rates from the market data."""
  
      forward_rates = market.reference_curve.get_forward_rate(
          self._accrual_start_dates,
          self._accrual_end_dates,
          self._daycount_fractions,
      )
  
      forward_rates = tf.where(
          self._daycount_fractions > 0.0,
          forward_rates,
          tf.zeros_like(forward_rates),
      )
  
      libor_rate = rc.get_rate_index(
          market,
          self._start_date,
          rc.RateIndexType.LIBOR,
          dtype=self._dtype,
      )
      libor_rate = tf.repeat(
          tf.convert_to_tensor(libor_rate, dtype=self._dtype),
          self._num_caplets,
      )
      forward_rates = tf.where(
          self._accrual_end_dates < valuation_date,
          tf.constant(0.0, dtype=self._dtype),
          tf.where(
              self._accrual_start_dates < valuation_date,
              libor_rate,
              forward_rates,
          ),
      )
  
      return forward_rates
  
file_path: tf-quant-finance/tf_quant_finance/experimental/instruments/cap_floor.py
imports: import tensorflow.compat.v2
  from tf_quant_finance import black_scholes
  from tf_quant_finance import datetime
  from tf_quant_finance.experimental.instruments import rates_common
label_desc: Use image node_id 5 for calling the CapAndFloor obj's underlying member method code with example usage: obj._get_forward_rate(valuation_date, market) and returns: forward_rates