class_docstr: A DenseBlock is a sequence of layers where each layer's outputs are concatenated with their inputs. This has the
  effect of accumulating outputs from previous layers as inputs to later ones and as the final output of the block.
  
  Args:
      layers: sequence of nn.Module objects to define the individual layers of the dense block
code: def forward(self, x):
      for l in self.children():
          result = l(x)
          x = torch.cat([x, result], 1)
  
      return x
  
file_path: MONAI/monai/networks/blocks/denseblock.py
imports: from __future__ import annotations
  from typing import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks import Convolution, ResidualUnit
  from monai.networks.layers.factories import Act, Norm
label_desc: Use image node_id 2 for calling the DenseBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x