class_docstr: Densenet based on: `Densely Connected Convolutional Networks <https://arxiv.org/pdf/1608.06993.pdf>`_.
  Adapted from PyTorch Hub 2D version: https://pytorch.org/vision/stable/models.html#id16.
  This network is non-deterministic When `spatial_dims` is 3 and CUDA is enabled. Please check the link below
  for more details:
  https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms
  
  Args:
      spatial_dims: number of spatial dimensions of the input image.
      in_channels: number of the input channel.
      out_channels: number of the output classes.
      init_features: number of filters in the first convolution layer.
      growth_rate: how many filters to add each layer (k in paper).
      block_config: how many layers in each pooling block.
      bn_size: multiplicative factor for number of bottle neck layers.
          (i.e. bn_size * k features in the bottleneck layer)
      act: activation type and arguments. Defaults to relu.
      norm: feature normalization type and arguments. Defaults to batch norm.
      dropout_prob: dropout rate after each dense layer.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x = self.features(x)
      x = self.class_layers(x)
      return x
  
imports: from __future__ import annotations
  import re
  from collections import OrderedDict
  from collections.abc import Callable, Sequence
  import torch
  import torch.nn
  from torch.hub import load_state_dict_from_url
  from monai.networks.layers.factories import Conv, Dropout, Pool
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils.module import look_up_option
label_desc: Use image node_id 2 for calling the DenseNet obj's underlying member method code with example usage: obj.forward(x) and returns: x