code: def forward(cls, ctx, logits, indices):
      # make sure we can use triton
      assert (
          indices.dtype == torch.int64
      ), "Indices are expected to be of type long."
      # make kernel
      device, dtype = logits.device, logits.dtype
      n_cols = logits.shape[-1]
      # run the kernel
      result = torch.empty_like(indices, dtype=dtype, device=device)
      neg_logprobs = torch.empty_like(
          logits, dtype=dtype, device=device
      )
      grid = lambda opt: (logits.numel() // n_cols,)
      _forward[grid](logits, neg_logprobs, indices, result, n_cols)
      # save for backward
      ctx.save_for_backward(neg_logprobs, indices)
      return result
  
file_path: triton/python/triton/ops/cross_entropy.py
imports: import torch
  from ..None import heuristics, jit
  from ..None import language
  from ..None import next_power_of_2
label_desc: Use image node_id 1 for calling the _cross_entropy obj's underlying member method code with example usage: obj.forward(cls, ctx, logits, indices) and returns: result