Debug mode that associates backward ops to forward ops in the trace

## 🚀 Feature

### Motivation

People that are not familiar with the autograd definitions for their models can have a hard time inspecting the `backward` trace generated by Thunder because it's not easy to say which section of backward operations relate to a specific forward operator.

Using a dummy example:

```python
import thunder
import torch

device = torch.device("cuda")

model = torch.nn.Linear(100, 10, bias=False, device=device)
x = torch.randn(10, 100, device=device)

fn = thunder.jit(model)
out = fn(x)
print(out.shape)

fwd_trace, bwd_trace = thunder.last_traces(fn)
print(fwd_trace[-1].python())
print("=" * 20)
print(bwd_trace[-1].python())
```

Generates

```python
# Constructed by Delete Last Used (took 0 milliseconds)
import torch
import torch.nn.functional
from thunder.executors.torchex import no_autocast

@torch.no_grad()
@no_autocast()
def augmented_forward_fn(t_0, t_weight):
# t_0: "cuda:0 f32[10, 100]"
# t_weight: "cuda:0 f32[10, 100]"
t0 = torch.nn.functional.linear(t_0, t_weight, None) # t0: "cuda:0 f32[10, 10]"
return {'output': t0, 'flat_args': [t_0, t_weight], 'flat_output': (t0,)}, ((t_0,), ())
```

```python
# Constructed by Delete Last Used (took 0 milliseconds)
import torch
from thunder.executors.torchex import no_autocast

@torch.no_grad()
@no_autocast()
def backward_fn(saved_for_backward, cotangents):
# saved_for_backward: "Collection"
# cotangents: "Collection"
C0, \
_, \
= saved_for_backward
clear_collection(saved_for_backward)
del saved_for_backward
t1, \
= cotangents
clear_collection(cotangents)
del cotangents
t_0, \
= C0
clear_collection(C0)
del C0
t6 = torch.reshape(t1, (-1, 10)) # t6: "cuda:0 f32[10, 10]"
del t1
t7 = torch.permute(t6, (1, 0)) # t7: "cuda:0 f32[10, 10]"
del t6
t8 = torch.reshape(t_0, (-1, 100)) # t8: "cuda:0 f32[10, 100]"
del t_0
t9 = torch.matmul(t7, t8) # t9: "cuda:0 f32[10, 100]"
del t7, t8
return (None, t9)
```

Since there's only one operation in forward, the link between traces is obvious.

But for a trace like that of LitGPT, with a lot more diversity and sequences of Transformer blocks, inspecting the backward trace is a challenge.

### Pitch

Since Thunder is all about debuggability, we could introduce a mode (assuming we don't want it to be default) where Thunder adds info about the linked forward operator. As an example, and extending the backward trace above:

```python
# Constructed by Delete Last Used (took 0 milliseconds)
import torch
from thunder.executors.torchex import no_autocast

@torch.no_grad()
@no_autocast()
def backward_fn(saved_for_backward, cotangents):
# saved_for_backward: "Collection"
# cotangents: "Collection"
C0, \
_, \
= saved_for_backward
clear_collection(saved_for_backward)
del saved_for_backward
t1, \
= cotangents
clear_collection(cotangents)
del cotangents
t_0, \
= C0
clear_collection(C0)
del C0
t6 = torch.reshape(t1, (-1, 10)) # t6: "cuda:0 f32[10, 10]", forward: t0: "torch.nn.functional.linear"
del t1
t7 = torch.permute(t6, (1, 0)) # t7: "cuda:0 f32[10, 10]", forward: t0: "torch.nn.functional.linear"
del t6
t8 = torch.reshape(t_0, (-1, 100)) # t8: "cuda:0 f32[10, 100]", forward: t0: "torch.nn.functional.linear"
del t_0
t9 = torch.matmul(t7, t8) # t9: "cuda:0 f32[10, 100]", forward: t0: "torch.nn.functional.linear"
del t7, t8
return (None, t9)
```

This is useful for debugging a specific section of your trace and also for learning autograd

cc @carmocca

Start with the thunder.jit and thunder.last_traces example in the issue, then inspect how forward and backward traces are produced and rendered. Done means an optional debug mode can associate backward operations with their forward operators in trace output without changing the default output.

Written by the indexing model from the issue text.