parameters: (self, axes=None, keepdims=1)
docstring: Args:
              axes (list of int): A list of integers, along which to reduce.
                  Accepted range is [-r, r-1] where r = rank(data). The default
                  is None, which reduces over all the dimensions of the input tensor.
              keepdims (int): Keep the reduced dimension or not, default 1 mean
                  keep reduced dimension.
docstring_summary: Args:
              axes (list of int): A list of integers, along which to reduce.
                  Accepted range is [-r, r-1] where r = rank(data). The default
                  is None, which reduces over all the dimensions of the input tensor.
              keepdims (int): Keep the reduced dimension or not, default 1 mean
                  keep reduced dimension.
docstring_tokens: Args : axes ( list of int ) : A list of integers along which to reduce . Accepted range is [ - r r - 1 ] where r = rank ( data ) . The default is None which reduces over all the dimensions of the input tensor . keepdims ( int ) : Keep the reduced dimension or not default 1 mean keep reduced dimension .
function: def __init__(self, axes=None, keepdims=1):
          """
          Args:
              axes (list of int): A list of integers, along which to reduce.
                  Accepted range is [-r, r-1] where r = rank(data). The default
                  is None, which reduces over all the dimensions of the input tensor.
              keepdims (int): Keep the reduced dimension or not, default 1 mean
                  keep reduced dimension.
          """
          super(ReduceMean, self).__init__()
          self.axes = axes
          self.keepdims = keepdims
function_tokens: def __init__ ( self , axes = None , keepdims = 1 ) : super ( ReduceMean , self ) . __init__ ( ) self . axes = axes self . keepdims = keepdims