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import torch
# https://pytorch.org/docs/stable/jit_builtin_functions.html#builtin-functions
class TSBuiltinOpsModule(torch.nn.Module):
def __init__(self):
super(TSBuiltinOpsModule, self).__init__()
def forward(self):
x = torch.tensor(1)
y = torch.tensor(0.5)
b = float(1)
s = "abcde"
l = ["1", "2", "test", "a{}b"]
d = {"key": 1}
d2 = {0: 100}
return len(
# type
bool(x),
bool(x.item()),
int(y),
int(y.item()),
float(x),
float(x.item()),
# math
x & x,
bool(x) & bool(x),
int(x) & int(x),
x | x,
bool(x) | bool(x),
int(x) | int(x),
x << x,
int(x) << int(x),
x >> x,
int(x) >> int(x),
x ^ x,
bool(x) ^ bool(x),
int(x) ^ int(x),
b * float(x),
b * int(x),
b + float(x),
b - float(x),
x.item() + y.item(),
x.item() - y.item(),
x.item() * y.item(),
x.item() / y.item(),
float(x) < float(y),
float(x) <= float(y),
float(x) > float(y),
float(x) > int(y),
float(x) >= float(y),
float(x) >= int(y),
float(x) == float(y),
float(x) == int(y),
float(x) != float(y),
int(x) != float(y),
float(x) / float(y),
int(x) / int(y),
max(x),
max(x.item(), y.item()),
max(int(x), int(y)),
max(float(x), float(y)),
min(x),
min(x.item(), y.item()),
min(int(x), int(y)),
min(float(x), float(y)),
int(l[0]),
float(l[0]),
# string
str(torch.tensor(1)),
l[2].find("t"),
l[2].replace("t", "x"),
l[2].lower(),
l[2].startswith("t"),
l[2].split("t"),
l[2].strip(),
l[2].rstrip(),
l[2].lstrip(),
l[2][slice(2)],
l[3].format("x"),
ord(l[2][0]),
len(torch.randn(3)),
len(l),
len(l[2]),
len(d),
len(d2),
)
class TSCollectionOpsModule(torch.nn.Module):
def __init__(self):
super(TSCollectionOpsModule, self).__init__()
def forward(self):
s = "abcde"
# list
l = ["1", "2", "test"]
l.reverse()
l.reverse()
l[1] = "3"
l.extend(["4"])
# str dict
d = {"key": 1}
d.clear()
d.update({"key": 0})
if "key" in d:
d["key"] = 2
# int dict
d2 = {0: 100}
if 0 in d2:
d2.clear()
d2[0] = 100
return len(
s[torch.tensor(1)],
d["key"],
d2[0],
d.keys(),
d.items(),
d.values(),
d2.values(),
l.pop(),
)
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