fix: setting torch frontend tensor 'data' attribute

[Sam-Armstrong]

Fixes setting the data attribute of a frontend tensor to match the behaviour of native torch, such as this minimal example which prior to this fix would throw AttributeError: can't set attribute 'data' for the frontend:

import torch
import ivy.functional.frontends.torch as torch_frontend

x = torch.randn((1, 3, 3))
y = torch.randn((1, 3, 3))
x.__setattr__('data', y)
print(x)

x = torch_frontend.randn((1, 3, 3))
y = torch_frontend.randn((1, 3, 3))
x.__setattr__('data', y)
print(x)

Does this look ok to you @Ishticode?

[github-actions]

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[Sam-Armstrong]

@Ishticode also, if this looks ok is there a way to stop the linter changing the code? 😅

[Ishticode]

was just playing with this.
One question i have is why do we want to just not do anything when data attribute is being updated. torch's native behaviour shows that the data changes. You can slightly modify this script to see. Whereas torch frontend data remains same before and after this.

print("native torch \n")
x = torch.randn((1, 3, 3))
y = torch.randn((1, 3, 3))
print("x.data before setattr:\n", x.data)
x.__setattr__('data', y)
print("x.data after setattr:\n", x.data)

print("ivy torch frontend\n")
x = torch_frontend.randn((1, 3, 3))
y = torch_frontend.randn((1, 3, 3))
print("x.data before setattr:\n")
print(x.data)
x.__setattr__('data', y)
print("x.data after setattr:\n")
print(x.data)

hmmm it does fix the issues you mentioned but it leads to this new subtle issue (which existed before ig but was explicit) which might be more troublesome elsewhere

[Sam-Armstrong]

@Ishticode my fix didn't have the pass, the linter added that for some reason. I was updating self