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# torch.clamp

torch.clamp(input, min=None, max=None, *, out=None) → Tensor

  Clamps all elements in `input` into the range [ `min`, `max` ]. Letting min_value and max_value be `min` and `max`, respectively, this returns:

  y i=min⁡(max⁡(x i,min_value i),max_value i)y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i) y i​=min(max(x i​,min_value i​),max_value i​)

  If `min` is `None`, there is no lower bound. Or, if `max` is `None` there is no upper bound.

  Note

  If `min` is greater than `max` `torch.clamp(..., min, max)` sets all elements in `input` to the value of `max`.

  Parameters:

    - input (Tensor) – the input tensor.

    - min (Number or Tensor, optional) – lower-bound of the range to be clamped to

    - max (Number or Tensor, optional) – upper-bound of the range to be clamped to

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> a = torch.randn(4)
      >>> a
      tensor([-1.7120,  0.1734, -0.0478, -0.0922])
      >>> torch.clamp(a, min=-0.5, max=0.5)
      tensor([-0.5000,  0.1734, -0.0478, -0.0922])

      >>> min = torch.linspace(-1, 1, steps=4)
      >>> torch.clamp(a, min=min)
      tensor([-1.0000,  0.1734,  0.3333,  1.0000])

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