@ptrblck thank you again for this reply. Could you please point me to an example of how to use the Identity function to achieve this? Would like to try it. BTW: the last layer in say mobilenet is a classifier
<.. features..>
    (18): ConvBNReLU(
      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU6(inplace=True)
    )
  )
  (classifier): Sequential(
    (0): Dropout(p=0.2, inplace=False)
    (1): Linear(in_features=1280, out_features=1000, bias=True)
  )
)
Sequential(
  (0): Dropout(p=0.2, inplace=False)
  (1): Linear(in_features=1280, out_features=1000, bias=True)

I presume I drop the classifier which is what I do with: self. pt_model=self.pt_model.features. Thus, the output is now from (2): ReLU6(inplace=True) - does this make sense? Would love to know how to do it via Identity method - just to be sure that it gives the same output.
Many thanks for all your help. REALLY appreciate it.