code: def layer_norm(
      x: torch.Tensor,
      weight: Optional[torch.Tensor] = None,
      bias: Optional[torch.Tensor] = None,
      eps: float = 1e-06,
  ) -> torch.Tensor:
      global _triton_registered_warnings
  
      r"""Applies normalization over a mini batch of inputs"""
  
      try:
          if (
              not _triton_registered_warnings
              and torch.cuda.is_available()
              and x.is_cuda
              and weight is not None
              and bias is not None
          ):
              return _LayerNorm.apply(x, weight, bias, eps)
      except RuntimeError as e:
          # Catch cases where the current GPU does not have enough registers to hold a full tensor line
          # fallback to PyTorch's implementation, which streams the tensor in and out
          _triton_registered_warnings = True
          logger.warning(
              "Triton layernorm kernel register spillover or invalid image caught. "
              "Deactivating this kernel, please file an issue in the xFormers repository"
          )
          logger.warning(e)
  
      return torch.nn.functional.layer_norm(
          x, [x.shape[-1]], weight=weight, bias=bias, eps=eps
      )
  
file_path: xformers/xformers/triton/layer_norm.py
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  import triton
  from torch.cuda.amp import custom_bwd, custom_fwd
  from xformers.triton.k_layer_norm import layer_norm_bwd_dwdb, layer_norm_bwd_dx_fused, layer_norm_fw
label_desc: Use image node_id 1 for calling a global function with example usage: layer_norm(x, weight, bias, eps) and returns: torch, _LayerNorm