docstr: temperature controlled sigmoid
  takes as input a torch tensor (tensor) and passes it through a sigmoid, controlled by temperature: temp
docstr_tok: ["temperature","controlled","sigmoid","takes","as","input","a","torch","tensor","(","tensor",")","and","passes","it","through","a","sigmoid",",","controlled","by","temperature",":","temp"]
code: def sigmoid(self, tensor, temp=1.0):
      """temperature controlled sigmoid
      takes as input a torch tensor (tensor) and passes it through a sigmoid, controlled by temperature: temp
      """
      exponent = -tensor / temp
      # clamp the input tensor for stability
      exponent = torch.clamp(exponent, min=-50, max=50)
      y = 1.0 / (1.0 + torch.exp(exponent))
      return y
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/pnp_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 3 for calling the PNPLoss obj's underlying member method code with example usage: obj.sigmoid(tensor, temp) and returns: y