Local variable serialization for workload training errors

### Problem Statement

Errors from training workloads (e.g. GPU) result in local variables that are unserializable or have repr/str methods that call out to a device. Attaching these local variables to Sentry results in segfault errors. While I have the option to not include any local variables with my error event payloads, this is an all or nothing approach, which can lead to the loss of valuable debug context.

### Solution Brainstorm

Framework-specific logic within the SDK and an accompanying flag to only extract tensor metadata. Here's an example:

```python
def extract_tensor_metadata(obj):
"""Safely extract metadata from tensor objects across frameworks"""
metadata = {'type': type(obj).__name__}

# Framework-specific safe extraction methods
if 'torch' in sys.modules and isinstance(obj, sys.modules['torch'].Tensor):
# PyTorch tensors
try:
metadata['shape'] = tuple(obj.shape)
metadata['dtype'] = str(obj.dtype)
metadata['device'] = str(obj.device)
metadata['requires_grad'] = bool(obj.requires_grad)
# Don't access .data or tensor contents
except Exception as e:
metadata['extraction_error'] = str(e)

elif 'jax.numpy' in sys.modules and isinstance(obj, sys.modules['jax.numpy'].ndarray):
# JAX arrays
try:
metadata['shape'] = obj.shape
metadata['dtype'] = str(obj.dtype)
# Safely extract device info
if hasattr(obj, 'device_buffer'):
try:
metadata['device'] = str(obj.device_buffer.device())
except:
metadata['device'] = 'unknown_jax_device'
except Exception as e:
metadata['extraction_error'] = str(e)

elif 'tensorflow' in sys.modules and isinstance(obj, sys.modules['tensorflow'].Tensor):
# TensorFlow tensors
try:
metadata['shape'] = tuple(obj.shape)
metadata['dtype'] = str(obj.dtype)
# TF device extraction can be complicated
try:
if hasattr(obj, 'device'):
metadata['device'] = str(obj.device)
elif hasattr(obj, '_device'):
metadata['device'] = str(obj._device)
except:
metadata['device'] = 'unknown_tf_device'
except Exception as e:
metadata['extraction_error'] = str(e)

# Add fallbacks for common attributes if not already set
for attr in ['shape', 'dtype']:
if attr not in metadata and hasattr(obj, attr):
try:
val = getattr(obj, attr)
metadata[attr] = str(val) if val is not None else None
except:
pass

return metadata
```

El cuerpo de la issue es el punto de partida disponible; revisa su problema de serialización de workload-training y el enfoque propuesto para los metadatos de tensor. No se nombran archivos, pruebas ni puntos de entrada, así que identifica la ruta de extracción de variables locales del SDK antes de definir un comportamiento seguro y la validación. La tarea estará terminada cuando se conserven metadatos útiles sin que el acceso al dispositivo provoque errores de serialización o segfaults.

Escrito por el modelo de indexación a partir del texto del issue.