code: def argmin(
      x: torch.Tensor,
      /,
      *,
      axis: Optional[int] = None,
      keepdims: bool = False,
      dtype: Optional[torch.dtype] = None,
      select_last_index: bool = False,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if select_last_index:
          if axis is None:
              x = torch.flip(x, dims=[axes for axes in range(x.ndim)])
              ret = torch.argmin(x, dim=axis, keepdim=keepdims)
              ret = x.numel() - ret - 1
          else:
              x = torch.flip(x, dims=(axis,))
              ret = torch.argmin(x, dim=axis, keepdim=keepdims)
              ret = x.shape[axis] - ret - 1
      else:
          ret = torch.argmin(x, dim=axis, keepdim=keepdims)
      if dtype:
          dtype = ivy.as_native_dtype(dtype)
          return ret.to(dtype=dtype)
      return ret
  
file_path: ivy/ivy/functional/backends/torch/searching.py
imports: from numbers import Number
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: argmin() and returns: ret, ret