class_docstr: Represents a basic subset object.
  
  Explanation
  ===========
  
  We generate subsets using essentially two techniques,
  binary enumeration and lexicographic enumeration.
  The Subset class takes two arguments, the first one
  describes the initial subset to consider and the second
  describes the superset.
  
  Examples
  ========
  
  >>> from sympy.combinatorics import Subset
  >>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
  >>> a.next_binary().subset
  ['b']
  >>> a.prev_binary().subset
  ['c']
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docstr: Return indices of subset in superset in a list; the list is empty
  if all elements of ``subset`` are not in ``superset``.
  
  Examples
  ========
  
      >>> from sympy.combinatorics import Subset
      >>> superset = [1, 3, 2, 5, 4]
      >>> Subset.subset_indices([3, 2, 1], superset)
      [1, 2, 0]
      >>> Subset.subset_indices([1, 6], superset)
      []
      >>> Subset.subset_indices([], superset)
      []
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code: def subset_indices(self, subset, superset):
      """Return indices of subset in superset in a list; the list is empty
      if all elements of ``subset`` are not in ``superset``.
  
      Examples
      ========
  
          >>> from sympy.combinatorics import Subset
          >>> superset = [1, 3, 2, 5, 4]
          >>> Subset.subset_indices([3, 2, 1], superset)
          [1, 2, 0]
          >>> Subset.subset_indices([1, 6], superset)
          []
          >>> Subset.subset_indices([], superset)
          []
  
      """
      a, b = superset, subset
      sb = set(b)
      d = {}
      for i, ai in enumerate(a):
          if ai in sb:
              d[ai] = i
              sb.remove(ai)
              if not sb:
                  break
      else:
          return []
      return [d[bi] for bi in b]
  
imports: from itertools import combinations
  from sympy.combinatorics.graycode import GrayCode
label_desc: Use image node_id 23 for calling the Subset obj's underlying member method code with example usage: obj.subset_indices(subset, superset) and returns: unknown, list