text: # Copyright 2020 InterDigital Communications, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import torch
  import torch.nn as nn
  
  
  class LowerBoundFunction(torch.autograd.Function):
      """Autograd function for the `LowerBound` operator.
      """
      @staticmethod
      def forward(ctx, input_, bound):
          ctx.save_for_backward(input_, bound)
          return torch.max(input_, bound)
  
      @staticmethod
      def backward(ctx, grad_output):
          input_, bound = ctx.saved_tensors
          pass_through_if = (input_ >= bound) | (grad_output < 0)
          return pass_through_if.type(grad_output.dtype) * grad_output, None
  
  
  class LowerBound(nn.Module):
      """Lower bound operator, computes `torch.max(x, bound)` with a custom
      gradient.
  
      The derivative is replaced by the identity function when `x` is moved
      towards the `bound`, otherwise the gradient is kept to zero.
      """
      def __init__(self, bound):
          super().__init__()
          self.register_buffer('bound', torch.Tensor([float(bound)]))
  
      @torch.jit.unused
      def lower_bound(self, x):
          return LowerBoundFunction.apply(x, self.bound)
  
      def forward(self, x):
          if torch.jit.is_scripting():
              return torch.max(x, self.bound)
          return self.lower_bound(x)
  