Incompatibility with HF Model Qwen2-1.5B - Tensor Indexing Error (1-D vs 2-D)

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## 🚀 Model / language coverage

I encountered an issue while attempting to use `thunder.jit` with models outside of the lit-gpt universe, specifically the Hugging Face model [Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct). The following error is thrown:

```
RuntimeError: Advanced indexing currently only supports zero or one-dimensional integer tensors, but found a tensor with dtype int64 and 2 dimensions.
```

The shape of the tensor in question is actually (1, 1024), which could potentially be handled with squeeze().

### Pitch

Supporting this case could enable compatibility with Qwen2-1.5B-Instruct and possibly with other models from Qwen family.

### Alternatives / Potential work-arounds

Adding the following code to the function `_advanced_indexing` in `thunder/clang/__init__.py` resolves the issue temporarily:

```py
if isinstance(x, TensorLike):
dims_to_squeeze = tuple([i for i, d in enumerate(x.shape) if d == 1])
if len(dims_to_squeeze) > 0:
x = prims.squeeze(x, dims_to_squeeze)
```

However, the same issue re-emerges in the prims.take_meta function:

```
RuntimeError: Expected index to be a 1-D or 0-D tensor, but index.ndim=2!
```

### Minimal Repro

```py
from transformers import AutoModelForCausalLM, AutoTokenizer
import thunder
import torch

# Define device and model
DEVICE = torch.device('cuda', 0)
model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2-1.5B-Instruct', torch_dtype=torch.bfloat16, device_map="cuda")

# Compilation with thunder.jit
model = thunder.jit(model)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")

# Sample input with shape (1, 1024)
inputs = tokenizer("Hello world!", return_tensors="pt").input_ids.to(DEVICE)
inputs = inputs.repeat(1, 1024 // inputs.shape[1])

# Forward pass
output = model(inputs)

```

results in

```
File "/home/mmikulski/miniconda3/envs/report2/lib/python3.10/site-packages/thunder/core/prims.py", line 2882, in take_meta
utils.check(index.ndim <= 1, lambda: f"Expected index to a 1-D or 0-D tensor, but index.ndim={index.ndim}!")
File "/home/mmikulski/miniconda3/envs/report2/lib/python3.10/site-packages/thunder/core/baseutils.py", line 103, in check
raise exception_type(s())
RuntimeError: Expected index to a 1-D or 0-D tensor, but index.ndim=2!
```

Reproduce the failure with the Qwen2-1.5B-Instruct example, then inspect _advanced_indexing in thunder/clang/__init__.py and take_meta in thunder/core/prims.py. Trace how the (1, 1024) index is handled in both locations; done means the reported model compilation and forward pass no longer raise the tensor-dimension errors.

Written by the indexing model from the issue text.