TorchScript

def loop_in_traced_fn(x):
    result = x[0]
    for i in range(x.size(0)):
        result = result * x[i]
    return result

inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]

traced = torch.jit.trace(loop_in_traced_fn, inputs, check_inputs=check_inputs)

ERROR: Graphs differed across invocations!
Graph diff:

            graph(%x : Tensor) {
            %1 : int = prim::Constant[value=0]()
            %2 : int = prim::Constant[value=0]()
            %result.1 : Tensor = aten::select(%x, %1, %2)
            %4 : int = prim::Constant[value=0]()
            %5 : int = prim::Constant[value=0]()
            %6 : Tensor = aten::select(%x, %4, %5)
            %result.2 : Tensor = aten::mul(%result.1, %6)
            %8 : int = prim::Constant[value=0]()
            %9 : int = prim::Constant[value=1]()
            %10 : Tensor = aten::select(%x, %8, %9)
        -   %result : Tensor = aten::mul(%result.2, %10)
        +   %result.3 : Tensor = aten::mul(%result.2, %10)
        ?          ++
            %12 : int = prim::Constant[value=0]()
            %13 : int = prim::Constant[value=2]()
            %14 : Tensor = aten::select(%x, %12, %13)
        +   %result : Tensor = aten::mul(%result.3, %14)
        +   %16 : int = prim::Constant[value=0]()
        +   %17 : int = prim::Constant[value=3]()
        +   %18 : Tensor = aten::select(%x, %16, %17)
        -   %15 : Tensor = aten::mul(%result, %14)
        ?     ^                                 ^
        +   %19 : Tensor = aten::mul(%result, %18)
        ?     ^                                 ^
        -   return (%15);
        ?             ^
        +   return (%19);
        ?             ^
            }

def fn(x):
    result = x[0]
    for i in range(x.size(0)):
        result = result * x[i]
    return result

inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]

scripted_fn = torch.jit.script(fn)
print(scripted_fn.graph)
#print(str(scripted_fn.graph).strip())

for input_tuple in [inputs] + check_inputs:
    torch.testing.assert_close(fn(*input_tuple), scripted_fn(*input_tuple))

graph(%x : Tensor) {
    %5 : bool = prim::Constant[value=1]()
    %1 : int = prim::Constant[value=0]()
    %result.1 : Tensor = aten::select(%x, %1, %1)
    %4 : int = aten::size(%x, %1)
    %result : Tensor = prim::Loop(%4, %5, %result.1)
    block0(%i : int, %7 : Tensor) {
        %10 : Tensor = aten::select(%x, %1, %i)
        %result.2 : Tensor = aten::mul(%7, %10)
        -> (%5, %result.2)
    }
    return (%result);
}

def fill_row_zero(x):
    x[0] = torch.rand(*x.shape[1:2])
    return x

traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)

fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
    x[0] = torch.rand(*x.shape[1:2])
fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%)
    traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
graph(%0 : Float(3, 4)) {
    return (%0);
}

def fill_row_zero(x):
    x = torch.cat((torch.rand(1, *x.shape[1:2]), x[1:2]), dim=0)
    return x

traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)

# Same behavior as pre-PyTorch 1.2
@torch.jit.script
def some_fn():
    return 2

# Marks a function as ignored, if nothing
# ever calls it then this has no effect
@torch.jit.ignore
def some_fn2():
    return 2

# As with ignore, if nothing calls it then it has no effect.
# If it is called in script it is replaced with an exception.
@torch.jit.unused
def some_fn3():
  import pdb; pdb.set_trace()
  return 4

# Doesn't do anything, this function is already
# the main entry point
@torch.jit.export
def some_fn4():
    return 2

from typing import Dict
import torch

class MyModule(torch.jit.ScriptModule):
    def __init__(self):
        super().__init__()
        self.my_dict = torch.jit.Attribute({}, Dict[str, int])
        self.my_int = torch.jit.Attribute(20, int)

m = MyModule()

from typing import Dict

class MyModule(torch.nn.Module):
    my_dict: Dict[str, int]

    def __init__(self):
        super().__init__()
        # This type cannot be inferred and must be specified
        self.my_dict = {}

        # The attribute type here is inferred to be `int`
        self.my_int = 20

    def forward(self):
        pass

m = torch.jit.script(MyModule())

class MyModule(torch.jit.ScriptModule):
    __constants__ = ['my_constant']

    def __init__(self):
        super().__init__()
        self.my_constant = 2

    def forward(self):
        pass
m = MyModule()

from typing import Final

class MyModule(torch.nn.Module):

    my_constant: Final[int]

    def __init__(self):
        super().__init__()
        self.my_constant = 2

    def forward(self):
        pass

m = torch.jit.script(MyModule())

import torch
from typing import Dict, Optional

@torch.jit.script
def make_dict(flag: bool):
    x: Dict[str, int] = {}
    x['hi'] = 2
    b: Optional[int] = None
    if flag:
        b = 2
    return x, b