class_docstr: Static restrictions stored in memory that are constant regardless of dt
  for each asset.
  
  Parameters
  ----------
  restricted_list : iterable of assets
      The assets to be restricted
class_docstr_tok: ["Static","restrictions","stored","in","memory","that","are","constant","regardless","of","dt","for","each","asset",".","Parameters","--","--","--","--","--","restricted_list",":","iterable","of","assets","The","assets","to","be","restricted"]
docstr: An asset is restricted for all dts if it is in the static list.
docstr_tok: ["An","asset","is","restricted","for","all","dts","if","it","is","in","the","static","list","."]
code: def is_restricted(self, assets, dt):
      """
      An asset is restricted for all dts if it is in the static list.
      """
      if isinstance(assets, Asset):
          return assets in self._restricted_set
      return pd.Series(
          index=pd.Index(assets),
          data=vectorized_is_element(assets, self._restricted_set),
      )
  
imports: import abc
  from numpy import vectorize
  from functools import partial, reduce
  import operator
  import pandas
  from six import with_metaclass, iteritems
  from collections import namedtuple
  from toolz import groupby
  from zipline.utils.enum import enum
  from zipline.utils.numpy_utils import vectorized_is_element
  from zipline.assets import Asset
label_desc: Use image node_id 2 for calling the StaticRestrictions obj's underlying member method code with example usage: obj.is_restricted(assets, dt) and returns: pd, unknown