Proxy renaming in general jit sometimes is skipped

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## 🐛 Bug

Proxy renaming in the initial trace doesn't work sometimes. Let's check how does the initial trace look like for the following example (taken from test_core.py::test_cse):
```py
import thunder
import torch

from thunder import clang

def func(x, y, device):
a = x * y
b = y / x
c = x * y
d = y / x
z = a * b
w = c * d
m = w * 1
a = clang.uniform(w.shape, device=device, dtype=thunder.float16)
return z, w, m, a

x = torch.randn(3, 4, device='cuda:0')
y = torch.randn(3, 4, device='cuda:0')
trace = thunder.trace()(func, x, y, 'cuda:0')
print(trace)
func = trace.python_callable()
jfunc = thunder.jit(func, executors=["torch"])
out = jfunc(x, y, device='cuda:0')
print(thunder.last_traces(jfunc)[0])
```
The initial trace in thunder.jit is
```py
def computation(x, y):
# x: "cuda:0 f32[3, 4]"
# y: "cuda:0 f32[3, 4]"

# thunder.func_39:15: t0 = ltorch.mul(x, y) # t0: "cuda:0 f32[3, 4]"
t0 = ltorch.mul(x, y) # t0: "cuda:0 f32[3, 4]"
# t0 = prims.mul(x, y) # t0: "cuda:0 f32[3, 4]"

# thunder.func_39:16: t1 = ltorch.true_divide(y, x) # t1: "cuda:0 f32[3, 4]"
t1 = ltorch.true_divide(y, x) # t1: "cuda:0 f32[3, 4]"
# t1 = prims.div(y, x) # t1: "cuda:0 f32[3, 4]"

# thunder.func_39:17: t2 = ltorch.mul(x, y) # t2: "cuda:0 f32[3, 4]"
t2 = ltorch.mul(x, y) # t2: "cuda:0 f32[3, 4]"
# t2 = prims.mul(x, y) # t2: "cuda:0 f32[3, 4]"

# thunder.func_39:18: t3 = ltorch.true_divide(y, x) # t3: "cuda:0 f32[3, 4]"
t3 = ltorch.true_divide(y, x) # t3: "cuda:0 f32[3, 4]"
# t3 = prims.div(y, x) # t3: "cuda:0 f32[3, 4]"

# thunder.func_39:19: t4 = ltorch.mul(t0, t1) # t4: "cuda:0 f32[3, 4]"
t4 = ltorch.mul(t0, t1) # t4: "cuda:0 f32[3, 4]"
# t4 = prims.mul(t0, t1) # t4: "cuda:0 f32[3, 4]"

# thunder.func_39:20: t5 = ltorch.mul(t2, t3) # t5: "cuda:0 f32[3, 4]"
t5 = ltorch.mul(t2, t3) # t5: "cuda:0 f32[3, 4]"
# t5 = prims.mul(t2, t3) # t5: "cuda:0 f32[3, 4]"

# thunder.func_39:21: t6 = ltorch.mul(t5, 1) # t6: "cuda:0 f32[3, 4]"
t6 = ltorch.mul(t5, 1) # t6: "cuda:0 f32[3, 4]"
# _ = prims.convert_element_type(1, float)
# t6 = prims.mul(t5, 1.0) # t6: "cuda:0 f32[3, 4]"

# thunder.func_39:22: t7 = prims.uniform((3, 4), 0.0, 1.0, device=devices.Device("cuda:0"), dtype=dtypes.float16) # t7: "cuda:0 f16[3, 4]"
t14 = prims.uniform((3, 4), 0.0, 1.0, device=devices.Device("cuda:0"), dtype=dtypes.float16) # t14: "cuda:0 f16[3, 4]"

# /home/iyashchuk/dev/pytorch/main/torch/autograd/grad_mode.py:186: torch._C._set_grad_enabled(mode)
return (t4, t5, t6, t14)
```
Why is `t14` not renamed to `t7` and all other variables are renamed?
The renaming is happening at https://github.com/Lightning-AI/lightning-thunder/blob/9f6e5b14e7a0fc6c96cca254540666d899df60b2/thunder/core/jit_ext.py#L1822-L1823

Start at thunder/core/jit_ext.py around lines 1822-1823, where the proxy renaming occurs, and reproduce the initial trace using the example from test_core.py::test_cse. Compare the generated names for the uniform operation and the other proxies; done means the initial trace consistently renames that proxy as expected rather than leaving it as t14.

Written by the indexing model from the issue text.