@constructor
def var(input, axis=None, ddof=0, keepdims=False, corrected=False):
 if isinstance(ddof, bool):
  raise ValueError('Parameter keepdims is now at index 3: (input,                           axis=None, ddof=0, keepdims=False, corrected=False)')
 input_ndim = input.type.ndim
 if (axis is None):
  axis = list(range(input_ndim))
 elif isinstance(axis, (integer_types, numpy.integer)):
  axis = [axis]
 elif (isinstance(axis, numpy.ndarray) and (axis.ndim == 0)):
  axis = [int(axis)]
 else:
  axis = [int(a) for a in axis]
 mean_input = mean(input, axis, keepdims=True)
 centered_input = (input - mean_input)
 two = constant(2, dtype=centered_input.dtype)
 if (ddof == 0):
  v = mean((centered_input ** two), axis, keepdims=keepdims)
 else:
  shp = (shape(input) - ddof)
  v = sum((centered_input ** two), axis=axis, keepdims=keepdims)
  for i in axis:
   v = true_div(v, shp[i])
 if corrected:
  if (ddof == 0):
   error = (mean(centered_input, axis, keepdims=keepdims) ** 2)
  else:
   shp = (shape(input) - ddof)
   shp_inp = shape(input)
   error = (sum(centered_input, axis=axis, keepdims=keepdims) ** 2)
   for i in axis:
    error = true_div(error, (shp[i] * shp_inp[i]))
  v = (v - error)
 v.name = 'var'
 return v

@ constructor def var ( input , axis = None , ddof = 0 , keepdims = False , corrected = False ) : if isinstance ( ddof , bool ) : raise ValueError ( 'Parameter keepdims is now at index 3: (input,                           axis=None, ddof=0, keepdims=False, corrected=False)' ) input_ndim = input . type . ndim if ( axis is None ) : axis = list ( range ( input_ndim ) ) elif isinstance ( axis , ( integer_types , numpy . integer ) ) : axis = [ axis ] elif ( isinstance ( axis , numpy . ndarray ) and ( axis . ndim == 0 ) ) : axis = [ int ( axis ) ] else : axis = [ int ( a ) for a in axis ] mean_input = mean ( input , axis , keepdims = True ) centered_input = ( input - mean_input ) two = constant ( 2 , dtype = centered_input . dtype ) if ( ddof == 0 ) : v = mean ( ( centered_input ** two ) , axis , keepdims = keepdims ) else : shp = ( shape ( input ) - ddof ) v = sum ( ( centered_input ** two ) , axis = axis , keepdims = keepdims ) for i in axis : v = true_div ( v , shp [ i ] ) if corrected : if ( ddof == 0 ) : error = ( mean ( centered_input , axis , keepdims = keepdims ) ** 2 ) else : shp = ( shape ( input ) - ddof ) shp_inp = shape ( input ) error = ( sum ( centered_input , axis = axis , keepdims = keepdims ) ** 2 ) for i in axis : error = true_div ( error , ( shp [ i ] * shp_inp [ i ] ) ) v = ( v - error ) v . name = 'var' return v
variance of a tensor .