class_docstr: Memory efficient implementation for training
  Follows recommendation from:
  https://github.com/lukemelas/EfficientNet-PyTorch/issues/18#issuecomment-511677853
  
  Results in ~ 30% memory saving during training as compared to Swish()
code: def backward(ctx, grad_output):
      input = ctx.saved_tensors[0]
      sigmoid_input = torch.sigmoid(input)
      return grad_output * (
          sigmoid_input * (1 + input * (1 - sigmoid_input))
      )
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from monai.utils import optional_import
label_desc: Use image node_id 2 for calling the SwishImplementation obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: unknown