class_docstr: Market data dictionary representation.
docstr: The spot price of an asset.
docstr_tok: ["The","spot","price","of","an","asset","."]
code: def spot(
      self,
      currency: List[str],
      asset: List[str],
      date: types.DateTensor = None,
  ) -> tf.Tensor:
      """The spot price of an asset."""
      spots = []
      for cur, s in zip(currency, asset):
          if s not in self.supported_assets(cur):
              raise ValueError(f"No data for asset {s}")
          data_spot = self._market_data_dict["equities"][cur][s]
          spots.append(
              tf.convert_to_tensor(data_spot["spot_price"], self._dtype)
          )
      return spots
  
imports: import datetime
  from typing import Dict, Any, List, Optional, Tuple
  import tensorflow.compat.v2
  from tf_quant_finance import datetime
  from tf_quant_finance import math
  from tf_quant_finance.experimental.pricing_platform.framework.core import currencies
  from tf_quant_finance.experimental.pricing_platform.framework.core import curve_types
  from tf_quant_finance.experimental.pricing_platform.framework.core import daycount_conventions
  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 volatility_surface
label_desc: Use image node_id 6 for calling the MarketDataDict obj's underlying member method code with example usage: obj.spot(currency, asset, date) and returns: spots