import torch

# Check for available backends
try:
    import torch_xla.core.xla_model as xm

    _XLA_AVAILABLE = True
except ImportError:
    _XLA_AVAILABLE = False

# Check for Apex availability for mixed precision
try:
    from apex import amp

    _APEX_AVAILABLE = True
except ImportError:
    _APEX_AVAILABLE = False


def astra(mixed_precision=False, fuse_kernels=False):
    def decorator(func):
        if mixed_precision and _APEX_AVAILABLE:
            func = amp.mixed_precision(func)

        def wrapper(*args, **kwargs):
            device = (
                "tpu"
                if _XLA_AVAILABLE and xm.xla_device().type == "xla"
                else ("gpu" if torch.cuda.is_available() else "cpu")
            )

            if device == "tpu":
                backend_args = [
                    xm._convert_to_xla_tensor(arg) if torch.is_tensor(arg) else arg
                    for arg in args
                ]
            else:
                backend_args = args

            # Apply kernel fusion, JIT, graph optimization, etc. here based on flags

            result = func(*backend_args, **kwargs)

            if device == "tpu":
                result = xm._convert_to_cpu_tensor(result)

            return result

        return wrapper

    return decorator


# Sample Usage

from torch import nn

data = torch.randn(2, 3)


@astra(mixed_precision=True)  # Potential optimizations applied.
def forward(x):
    softmax = nn.Softmax(dim=1)
    result = softmax(x)
    return result


result = forward(data)
print(result)
