def __init__(self, start, limit=None, delta=1):
super(Range, self).__init__()
validator.check_value_type("start", start, [int, float], self.cls_name)
validator.check_value_type("delta", delta, [int, float], self.cls_name)
if delta == 0:
raise ValueError("The input of `delta` can not be equal to zero.")
if limit is not None:
validator.check_value_type("limit", limit, [int, float], self.cls_name)
if isinstance(start, int) and isinstance(limit, int) and isinstance(delta, int):
self.dtype = mstype.int32
else:
self.dtype = mstype.float32
else:
if isinstance(start, int) and isinstance(delta, int):
self.dtype = mstype.int32
else:
self.dtype = mstype.float32
if isinstance(start, int):
start = float(start)
if isinstance(limit, int):
limit = float(limit)
if isinstance(delta, int):
delta = float(delta)
self.range_x = inner.Range(start, limit, delta)
if limit is None:
length_input = math.ceil(start / delta)
else:
length_input = math.ceil((limit - start) / delta)
self.input_tensor = Tensor(list(range(length_input)), self.dtype)
def __init__(self):
super(RangeNet, self).__init__()
self.range_ops = inner.Range(1.0, 8.0, 2.0)
