code: def argmin(
      x: paddle.Tensor,
      /,
      *,
      axis: Optional[int] = None,
      keepdims: bool = False,
      dtype: Optional[paddle.dtype] = None,
      select_last_index: bool = False,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      dtype = dtype if dtype is not None else paddle.int64
      if select_last_index:
          x = paddle_backend.flip(x, axis=axis)
          ret = paddle.argmin(x, axis=axis, keepdim=keepdims)
          if axis is not None:
              ret = paddle.to_tensor(x.shape[axis] - ret - 1)
          else:
              ret = paddle.to_tensor(x.size - ret - 1)
      else:
          ret = paddle.argmin(x, axis=axis, keepdim=keepdims)
  
      if keepdims and axis is None:
          ret = ret.reshape([1] * x.ndim)
      if not keepdims and (x.ndim == 1 or axis is None):
          ret = paddle_backend.squeeze(ret, axis=-1)
      return ret.astype(dtype)
  
file_path: ivy/ivy/functional/backends/paddle/searching.py
imports: from numbers import Number
  from typing import Optional, Tuple, Union
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
  from .None import backend_version
  from .elementwise import _elementwise_helper
label_desc: Use image node_id 2 for calling a global function with example usage: argmin() and returns: ret