class_docstr: Hard Sigmoid Module. Apply the hard sigmoid function:
  Hsigmoid(x) = min(max((x + bias) / divisor, min_value), max_value)
  Default: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1)
  
  Args:
      bias (float): Bias of the input feature map. Default: 1.0.
      divisor (float): Divisor of the input feature map. Default: 2.0.
      min_value (float): Lower bound value. Default: 0.0.
      max_value (float): Upper bound value. Default: 1.0.
  
  Returns:
      Tensor: The output tensor.
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code: def forward(self, x):
      x = (x + self.bias) / self.divisor
  
      return x.clamp_(self.min_value, self.max_value)
  
imports: import torch.nn
  from .registry import ACTIVATION_LAYERS
label_desc: Use image node_id 2 for calling the HSigmoid obj's underlying member method code with example usage: obj.forward(x) and returns: x