parameters: (a, axis, ddof=0, keepdims=False)
return_statement: return divide(sum_a, F.tensor_sub(count, ddof))
docstring: Computes nanvar without applying keyword arguments.
docstring_summary: Computes nanvar without applying keyword arguments.
docstring_tokens: Computes nanvar without applying keyword arguments .
function: def _nanvar(a, axis, ddof=0, keepdims=False):
      """Computes nanvar without applying keyword arguments."""
      mean_a = nanmean(a, axis=axis, keepdims=True)
      pow_a = F.tensor_pow(F.tensor_sub(a, mean_a), 2)
      sum_a = _reduce_nansum(pow_a, axis, keepdims)
      count = _count_nonnan(a, axis, keepdims)
      return divide(sum_a, F.tensor_sub(count, ddof))
function_tokens: def _nanvar ( a , axis , ddof = 0 , keepdims = False ) : mean_a = nanmean ( a , axis = axis , keepdims = True ) pow_a = F . tensor_pow ( F . tensor_sub ( a , mean_a ) , 2 ) sum_a = _reduce_nansum ( pow_a , axis , keepdims ) count = _count_nonnan ( a , axis , keepdims ) return divide ( sum_a , F . tensor_sub ( count , ddof ) )