code: def create_mask(x):
      """Given a list of object creates integer mask for unique values in the list.
  
      Args:
        x: 1-d numpy array.
  
      Returns:
        A tuple of three objects:
          * A list of integers that is the mask for `x`,
          * A dictionary map between  entries of `x` and the list
          * The number of unique elements.
      """
      # For example, create_mask(["USD", "AUD", "USD"]) returns
      # a list [1, 0, 1], a map {0: "AUD", 1: "USD"} and the number of unique
      # elements which is 2.
      # Note that elements of `x` are being sorted
      unique = np.unique(x)
      num_unique_elems = len(unique)
      keys = range(num_unique_elems)
      d = dict(zip(unique, keys))
      mask_map = dict(zip(keys, unique))
      return [d[el] for el in x], mask_map, num_unique_elems
  
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 5 for calling a global function with example usage: create_mask(x) and returns: unknown,  mask_map,  num_unique_elems