class_docstr: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
  https://arxiv.org/abs/1611.09326
  
  In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art
  results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor
  pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently
  published best entries for these datasets.
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code: def predict(self, x):
      logits = self(x)
      return F.softmax(logits)
  
file_path: pywick/pywick/models/segmentation/testnets/fc_densenet.py
imports: from typing import Optional, Sequence, Union
  from torch.nn import Module, Conv2d, BatchNorm2d, Linear, init
  from torch.nn import functional
  from .transition_down import TransitionDown
  from .transition_up import TransitionUp
  from .dense_block import DenseBlock
label_desc: Use image node_id 3 for calling the FCDenseNet obj's underlying member method code with example usage: obj.predict(x) and returns: F