class_docstr: Clamp activation layer.
  
  This activation function is to clamp the feature map value within
  :math:`[min, max]`. More details can be found in ``torch.clamp()``.
  
  Args:
      min (Number | optional): Lower-bound of the range to be clamped to.
          Default to -1.
      max (Number | optional): Upper-bound of the range to be clamped to.
          Default to 1.
class_docstr_tok: ["Clamp","activation","layer",".","This","activation","function","is","to","clamp","the","feature","map","value","within",":","math",":","`","[","min",",","max","]","`",".","More","details","can","be","found","in","``","torch.clamp","(",")","``",".","Args",":","min","(","Number","|","optional",")",":","Lower-bound","of","the","range","to","be","clamped","to",".","Default","to","-1.","max","(","Number","|","optional",")",":","Upper-bound","of","the","range","to","be","clamped","to",".","Default","to","1","."]
code: def __init__(self, min=-1.0, max=1.0):
      super(Clamp, self).__init__()
      self.min = min
      self.max = max
  
file_path: lavis/lavis/common/annotator/uniformer/mmcv/cnn/bricks/activation.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from annotator.uniformer.mmcv.utils import TORCH_VERSION, build_from_cfg, digit_version
  from .registry import ACTIVATION_LAYERS
label_desc: Use image node_id 1 to create a new Clamp object from inherited base classes: nn with example: obj = Clamp(min, max)