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 present value of the Cap/Floor on the valuation date.
  
  Args:
    valuation_date: A scalar `DateTensor` specifying the date on which
      valuation is being desired.
    market: A namedtuple of type `InterestRateMarket` which contains the
      necessary information for pricing the Cap/Floor.
    model: An optional input of type `InterestRateModelType` to specify which
      model to use for pricing.
      Default value: `None` in which case `LOGNORMAL_RATE` model is used.
    pricing_context: An optional input to provide additional parameters (such
      as model parameters) relevant for pricing.
    name: Python str. The name to give to the ops created by this function.
      Default value: `None` which maps to `"price"`.
  
  Returns:
    A Rank 1 `Tensor` of real type containing the modeled price of each cap
    (or floor) based on the input market data.
  
  Raises:
    ValueError: If an unsupported model is supplied to the function.
code: def price(
      self,
      valuation_date,
      market,
      model=None,
      pricing_context=None,
      name=None,
  ):
      """Returns the present value of the Cap/Floor on the valuation date.
  
      Args:
        valuation_date: A scalar `DateTensor` specifying the date on which
          valuation is being desired.
        market: A namedtuple of type `InterestRateMarket` which contains the
          necessary information for pricing the Cap/Floor.
        model: An optional input of type `InterestRateModelType` to specify which
          model to use for pricing.
          Default value: `None` in which case `LOGNORMAL_RATE` model is used.
        pricing_context: An optional input to provide additional parameters (such
          as model parameters) relevant for pricing.
        name: Python str. The name to give to the ops created by this function.
          Default value: `None` which maps to `"price"`.
  
      Returns:
        A Rank 1 `Tensor` of real type containing the modeled price of each cap
        (or floor) based on the input market data.
  
      Raises:
        ValueError: If an unsupported model is supplied to the function.
      """
  
      model = model or rc.InterestRateModelType.LOGNORMAL_RATE
      name = name or (self._name + "_price")
      with tf.name_scope(name):
          valuation_date = dates.convert_to_date_tensor(valuation_date)
          if model == rc.InterestRateModelType.LOGNORMAL_RATE:
              caplet_prices = self._price_lognormal_rate(
                  valuation_date, market, pricing_context
              )
          else:
              raise ValueError(f"Unsupported model {model}.")
  
          return tf.math.segment_sum(
              caplet_prices, self._contract_index
          )
  
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 2 for calling the CapAndFloor obj's underlying member method code with example usage: obj.price(valuation_date, market, model, pricing_context, name) and returns: tf