Task list to jit `Adam.step`

Nobody has claimed this yet.

This is a list of tasks to enable compiling `Adam.step`/`AdamW.step`.

## Goal

The expected usage is:

```py
optimizer = Adam(model.parameters())
optimizer_step = jit(optimizer.step)
# or torch.compile(optimizer.step, backend=)

for X, y in data:
optimizer.zero_grad()
outputs = model(X)
loss = loss_fn(X, y)
loss.backward()
optimizer_step()
```

## Task list for `torch.compile(optimizer.step, backend=)`

We have to be able to trace through PyTorch's implementation of `Adam.step` (with `foreach=False`, `fused=False`)

- [x] #1070
Turned out to be not necessary
- [x] #1062

## Task list for `thunder.jit(optimizer.step)`

~This can be easily achieved by applying an `OperatorExecutor` to symbolically replace `Adam.step`'s inner implementation with our own thunder-friendly implementation.~
This is no longer needed thanks to improved support for in-place ops.

We need the following to trace `Adam.step`:
* `_single_tensor_adam` function
- [ ] Add `Tensor.conj`
- [ ] Cast the computation result of the in-place operation into the dst tensor's dtype
Specifically, we must support `(bloat16[N]).addcdiv_(bfloat16[N], bfloat16[N], value=float32)`
This was done in [#1194](https://github.com/Lightning-AI/lightning-thunder/pull/1194)
* Inner state tensor management
- [ ] Add `memory_format` option to `zeros_like`
- [ ] #1057

Start by reading PyTorch's `_single_tensor_adam` implementation and the issue linked as #1057, then inspect the existing support for `Tensor.conj`, dtype casting in `addcdiv_`, and `zeros_like(memory_format)`. Done means the listed tracing requirements are implemented so `thunder.jit(optimizer.step)` can compile both Adam and AdamW step paths.

Written by the indexing model from the issue text.