code: def fatmin(
      x: Tensor,
      dim: Union[int, Tuple[int, ...]],
      keepdim: bool = False,
      tau: Union[float, Tensor] = TAU,
      alpha: float = ALPHA,
  ) -> Tensor:
      """Computes a smooth approximation to amin(X, dim=dim) with a fat tail.
  
      Args:
          X: A Tensor from which to compute the smoothed minimum.
          dim: The dimensions to reduce over.
          keepdim: If True, keeps the reduced dimensions.
          tau: Temperature parameter controlling the smooth approximation
              to min operator, becomes tighter as tau goes to 0. Needs to be positive.
          alpha: The exponent of the asymptotic power decay of the approximation. The
              default value is 2. Higher alpha parameters make the function behave more
              similarly to the standard logsumexp approximation to the max, so it is
              recommended to keep this value low or moderate, e.g. < 10.
  
      Returns:
          A Tensor of smooth approximations to `amin(X, dim=dim)` with a fat tail.
      """
      return -fatmax(-x, dim=dim, keepdim=keepdim, tau=tau, alpha=alpha)
  
imports: from __future__ import annotations
  import math
  from typing import Callable, Tuple, Union
  import torch
  from botorch.exceptions import UnsupportedError
  from botorch.utils.constants import get_constants_like
  from torch import finfo, Tensor
  from torch.nn.functional import softplus
label_desc: Use image node_id 23 for calling a global function with example usage: fatmin(x, dim, keepdim, tau, alpha) and returns: unknown