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 forward(ctx, input):
      result = input * torch.sigmoid(input)
      ctx.save_for_backward(input)
      return result
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from monai.utils import optional_import
label_desc: Use image node_id 1 for calling the SwishImplementation obj's underlying member method code with example usage: obj.forward(ctx, input) and returns: result