class_docstr: Abstract source of variables.
  
  Objects which implement this interface provide a source of variables, returned
  as a collection of (nested) numpy arrays. Generally this will be used to
  provide variables to some learned policy/etc.
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docstr: Return the named variables as a collection of (nested) numpy arrays.
  
  Args:
    names: args where each name is a string identifying a predefined subset of
      the variables.
  
  Returns:
    A list of (nested) numpy arrays `variables` such that `variables[i]`
    corresponds to the collection named by `names[i]`.
docstr_tok: ["Return","the","named","variables","as","a","collection","of","(","nested",")","numpy","arrays",".","Args",":","names",":","args","where","each","name","is","a","string","identifying","a","predefined","subset","of","the","variables",".","Returns",":","A","list","of","(","nested",")","numpy","arrays","`","variables","`","such","that","`","variables","[","i","]","`","corresponds","to","the","collection","named","by","`","names","[","i","]","`","."]
code: def get_variables(
      self, names: Sequence[str]
  ) -> List[types.NestedArray]:
      """Return the named variables as a collection of (nested) numpy arrays.
  
      Args:
        names: args where each name is a string identifying a predefined subset of
          the variables.
  
      Returns:
        A list of (nested) numpy arrays `variables` such that `variables[i]`
        corresponds to the collection named by `names[i]`.
      """
  
imports: import abc
  import itertools
  from typing import Generic, Iterator, List, Optional, Sequence, TypeVar
  from acme import types
  from acme.utils import metrics
  import dm_env
label_desc: Use image node_id 1 for calling the VariableSource obj's underlying member method code with example usage: obj.get_variables(names) without return types