class_docstr: Applies the element-wise function:
  
  :math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the
  zero_point, and :math:`q(6)` is the quantized representation of number 6.
  
  Args:
      inplace: can optionally do the operation in-place. Default: ``False``
  
  Shape:
      - Input: :math:`(N, *)` where `*` means, any number of additional
        dimensions
      - Output: :math:`(N, *)`, same shape as the input
  
  .. image:: ../scripts/activation_images/ReLU6.png
  
  Examples::
  
      >>> m = nn.quantized.ReLU6()
      >>> input = torch.randn(2)
      >>> # xdoctest: +SKIP
      >>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
      >>> output = m(input)
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code: def __init__(self, inplace=False):
      super().__init__(inplace)
      self.inplace = inplace
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 1 to create a new ReLU6 object from inherited base classes: torch.nn with example: obj = ReLU6(inplace)