class_docstr: Predict the density map with DenseNet
  
  Example:
      >>> import torch
      >>> from towhee.models.transrac import DenseMap
      >>>
      >>> dummy_input = torch.rand(3)
      >>> dense_map = DenseMap(input_dim=3, hidden_dim_1=8, hidden_dim_2=8, out_dim=5)
      >>> out = dense_map(dummy_input)
      >>> print(out.shape)
      torch.Size([5])
code: def forward(self, x):
      x = self.layers(x)
      return x
  
file_path: towhee/towhee/models/transrac/utils.py
imports: from torch import nn
  from towhee.models.layers.attention import Attention
label_desc: Use image node_id 2 for calling the DenseMap obj's underlying member method code with example usage: obj.forward(x) and returns: x