class_docstr: Represents a batch of cashflows indexed to a floating rate.
class_docstr_tok: ["Represents","a","batch","of","cashflows","indexed","to","a","floating","rate","."]
docstr: Returns cashflows for the floating leg.
  
  Args:
    market: An instance of `ProcessedMarketData`.
    past_fixing: An optional `Tensor` of shape compatible with
      `batch_shape + [1]`. Represents the fixings for the cashflows as
      observed at `market.date`.
    name: Python str. The name to give to the ops created by this function.
      Default value: `None` which maps to 'cashflows'.
  
  Returns:
    A tuple of two `Tensor`s of shape `batch_shape + [num_cashflows]` and
    containing the dates and the corresponding cashflows price for each
    stream based on the input market data.
code: def cashflows(
      self,
      market: pmd.ProcessedMarketData,
      past_fixing: Optional[types.FloatTensor] = None,
      name: Optional[str] = None,
  ) -> Tuple[types.DateTensor, types.FloatTensor]:
      """Returns cashflows for the floating leg.
  
      Args:
        market: An instance of `ProcessedMarketData`.
        past_fixing: An optional `Tensor` of shape compatible with
          `batch_shape + [1]`. Represents the fixings for the cashflows as
          observed at `market.date`.
        name: Python str. The name to give to the ops created by this function.
          Default value: `None` which maps to 'cashflows'.
  
      Returns:
        A tuple of two `Tensor`s of shape `batch_shape + [num_cashflows]` and
        containing the dates and the corresponding cashflows price for each
        stream based on the input market data.
      """
      name = name or (self._name + "_cashflows")
      with tf.name_scope(name):
          _, forward_rates = self.forward_rates(
              market, past_fixing=past_fixing
          )
  
          coupon_rate = forward_rates + tf.expand_dims(
              self._spread, axis=-1
          )
          # self._notion is of shape [batch_shape], so broadcasting is needed
          notional = tf.expand_dims(self._notional, axis=-1)
  
          cashflows = notional * (
              self._daycount_fractions * coupon_rate
          )
          return self._coupon_end_dates, cashflows
  
imports: from typing import Optional, Tuple, Callable, Any, List, Union
  import numpy
  import tensorflow.compat.v2
  from tf_quant_finance import datetime
  from tf_quant_finance.experimental.pricing_platform.framework.core import curve_types
  from tf_quant_finance.experimental.pricing_platform.framework.core import processed_market_data
  from tf_quant_finance.experimental.pricing_platform.framework.core import types
  from tf_quant_finance.experimental.pricing_platform.framework.market_data import rate_curve
  from tf_quant_finance.experimental.pricing_platform.framework.market_data import utils
  from tf_quant_finance.experimental.pricing_platform.framework.rate_instruments import coupon_specs
  from tf_quant_finance.experimental.pricing_platform.instrument_protos import period_pb2
  from tf_quant_finance.math import pad
label_desc: Use image node_id 12 for calling the FloatingCashflowStream obj's underlying member method code with example usage: obj.cashflows(market, past_fixing, name) and returns: self,  cashflows