code: def softshrink(a: TensorLikeType, lambd: float = 0.5):
      # Formula for reference,
      # softshrink(x) = x - lambd if x > lambd
      #               = x + lambd if x < -lambd
      #               = 0 otherwise
      torch._check(
          lambd >= 0,
          lambda: f"lambda must be greater or equal to 0, but found to be {lambd}",
      )
      # We implement this in one torch.where to generate better code in the backward
      # see https://github.com/pytorch/pytorch/pull/107052#discussion_r1293748211
      return torch.where(
          torch.abs(a) > lambd, a - torch.sign(a) * lambd, 0
      )
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 17 for calling a global function with example usage: softshrink(a, lambd) and returns: torch