feat: handle case for float values with int dtype specified in tf backend

[Ishticode]

Previously

import torch
import ivy
import ivy.functional.frontends.torch as torch_frontend
print(torch.tensor(0.5, dtype=torch.int64)) # works - creates tensor(0)

ivy.set_tensorflow_backend()
print(torch_frontend.tensor(0.5, dtype='int64')) # throws error

But after this the frontend torch will create ivy.frontends.torch.Tensor(0)

[github-actions]

PR Compliance Checks

Thank you for your Pull Request! We have run several checks on this pull request in order to make sure it's suitable for merging into this project. The results are listed in the following section.

Issue Reference

In order to be considered for merging, the pull request description must refer to a specific issue number. This is described in our contributing guide and our PR template.
This check is looking for a phrase similar to: "Fixes #XYZ" or "Resolves #XYZ" where XYZ is the issue number that this PR is meant to address.

[vedpatwardhan]

Hey @Ishticode, thanks for creating the PR 😄
I think this is something we should do in the frontends because passing in a float input and specifying the int dtype is quite strange behaviour which should be treated as framework-specific and dealt with in the frontends. What do you think?

[Ishticode]

best handled in backend bcz all frontends support in native. e.g.
jnp.array(0.5, dtype=jnp.int64) -> array(0)
np.array(0.5, dtype=np.int64) -> array(0)
😄