class_docstr: DenseNet169 with optional pretrained support when `spatial_dims` is 2.
code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      init_features: int = 64,
      growth_rate: int = 32,
      block_config: Sequence[int] = (6, 12, 32, 32),
      pretrained: bool = False,
      progress: bool = True,
      **kwargs,
  ) -> None:
      super().__init__(
          spatial_dims=spatial_dims,
          in_channels=in_channels,
          out_channels=out_channels,
          init_features=init_features,
          growth_rate=growth_rate,
          block_config=block_config,
          **kwargs,
      )
      if pretrained:
          if spatial_dims > 2:
              raise NotImplementedError(
                  "Parameter `spatial_dims` is > 2 ; currently PyTorch Hub does not"
                  "provide pretrained models for more than two spatial dimensions."
              )
          _load_state_dict(self, "densenet169", progress)
  
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 1 to create a new DenseNet169 object from inherited base classes: DenseNet with example: obj = DenseNet169(spatial_dims, in_channels, out_channels, init_features, growth_rate, block_config, pretrained, progress)