torch.compile() supports many different backends but one that we’re particularly excited about is Inductor which generates Triton kernels https://github.com/openai/triton which are written in Python yet outperform the vast majority of handwritten CUDA kernels. Suppose our example above was called trig.py we can actually inspect the code generated triton kernels by running.
TORCH_COMPILE_DEBUG=1 python trig.py

```python
@pointwise(size_hints=[16384], filename=file, meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': 0, 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]})
@triton.jit
def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 10000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.reshape(tl.arange(0, XBLOCK), [XBLOCK])
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.sin(tmp0)
    tmp2 = tl.sin(tmp1)