Different torchvision.resnet output depending on batch size in eval mode

Hi all,
It seems like models in eval mode return different outputs depending on the batch size of the input provided.
It is my understanding this is not expected.
I already checked here, here (and in other 4 posts but the new user limit limits me to 2 links) but the answer is not obvious to me.
I created some dummy code to demonstrated the problem I’m having.
Any Idea if this is expected or why it is happening?

torch version: 1.10.1
python version: 3.9.7

import torch
import torchvision

model = torchvision.models.resnet18(pretrained=True, progress=True)

a = torch.ones((1, 3, 96, 96))
b = torch.ones((64, 3, 96, 96))

with torch.no_grad():
    a_out = model(a)
    b_out = model(b)

diff = torch.absolute(b_out[0] - a_out[0])
total_diff = torch.sum(diff)
average_diff = torch.mean(diff)
print(f"equal: {torch.equal(a_out[0], b_out[0])}")
print(f"rtol=0.00001: {torch.allclose(a_out[0], b_out[0])}")
print(f"rtol=0.0001: {torch.allclose(a_out[0], b_out[0], rtol=0.0001)}")
print(f"rtol=0.001: {torch.allclose(a_out[0], b_out[0], rtol=0.001)}")
print(f"rtol=0.01: {torch.allclose(a_out[0], b_out[0], rtol=0.01)}")
print(f"rtol=0.1: {torch.allclose(a_out[0], b_out[0], rtol=0.1)}")
print(f"total diff {total_diff}")
print(f"avg diff {average_diff}")

And this is the output:

equal: False
rtol=0.00001: False
rtol=0.0001: False
rtol=0.001: True
rtol=0.01: True
rtol=0.1: True
total diff 0.04874414950609207
avg diff 7.616273478561197e-07