code: def layer_norm(
      x: torch.Tensor,
      normalized_idxs: List[int],
      /,
      *,
      scale: Optional[torch.Tensor] = None,
      offset: Optional[torch.Tensor] = None,
      eps: float = 1e-05,
      new_std: float = 1.0,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      normalized_shape = x.shape[normalized_idxs[0] :]
      xnormalized = torch.nn.functional.layer_norm(
          x, normalized_shape, weight=scale, bias=offset, eps=eps
      )
      return torch.multiply(xnormalized, new_std)
  
imports: import torch
  from typing import Optional, List
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
label_desc: Use image node_id 1 for calling a global function with example usage: layer_norm() and returns: torch