code: def log_softmax(
      x: torch.Tensor,
      /,
      *,
      axis: Optional[int] = -1,
      complex_mode: Literal["split", "magnitude", "jax"] = "jax",
      out: Optional[torch.Tensor] = None,
  ):
      if torch.is_complex(x):
          x_max = torch_backend.max(x, axis=axis, keepdims=True)
          sub_temp = torch.sub(x, x_max)
          ret = torch.sum(sub_temp.exp(), dim=axis, keepdim=True)
          ret = torch.log(ret)
          return torch.sub(sub_temp, ret)
      return torch.nn.functional.log_softmax(x, axis)
  
file_path: ivy/ivy/functional/backends/torch/activations.py
imports: from typing import Optional, Union, Literal
  import numpy
  import torch
  import torch.nn
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
  import ivy.functional.backends.torch
label_desc: Use image node_id 8 for calling a global function with example usage: log_softmax() and returns: torch, torch