class_docstr: This analyzer calculates the Gross Leverage of the current strategy
  on a timeframe basis
  
  Params:
  
    - ``fund`` (default: ``None``)
  
      If ``None`` the actual mode of the broker (fundmode - True/False) will
      be autodetected to decide if the returns are based on the total net
      asset value or on the fund value. See ``set_fundmode`` in the broker
      documentation
  
      Set it to ``True`` or ``False`` for a specific behavior
  
  Methods:
  
    - get_analysis
  
      Returns a dictionary with returns as values and the datetime points for
      each return as keys
class_docstr_tok: ["This","analyzer","calculates","the","Gross","Leverage","of","the","current","strategy","on","a","timeframe","basis","Params",":","-","``","fund","``","(","default",":","``","None","``",")","If","``","None","``","the","actual","mode","of","the","broker","(","fundmode","-","True\/False",")","will","be","autodetected","to","decide","if","the","returns","are","based","on","the","total","net","asset","value","or","on","the","fund","value",".","See","``","set_fundmode","``","in","the","broker","documentation","Set","it","to","``","True","``","or","``","False","``","for","a","specific","behavior","Methods",":","-","get_analysis","Returns","a","dictionary","with","returns","as","values","and","the","datetime","points","for","each","return","as","keys"]
code: def next(self):
      # Updates the leverage for "dtkey" (see base class) for each cycle
      # 0.0 if 100% in cash, 1.0 if no short selling and fully invested
      lev = (self._value - self._cash) / self._value
      self.rets[self.data0.datetime.datetime()] = lev
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import backtrader
label_desc: Use image node_id 3 for calling the GrossLeverage obj's underlying member method code with example usage: obj.next() without return types