class_docstr: Vision Transformers for Dense Prediction.
  This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
  Args:
      embed_dims (int): The embed dimension of the ViT backbone.
          Default: 768.
      post_process_channels (List): Out channels of post process conv
          layers. Default: [96, 192, 384, 768].
      readout_type (str): Type of readout operation. Default: 'ignore'.
      patch_size (int): The patch size. Default: 16.
      expand_channels (bool): Whether expand the channels in post process
          block. Default: False.
code: def forward(self, inputs, img_metas):
      assert len(inputs) == self.num_reassemble_blocks
      x = [inp for inp in inputs]
      x = self.reassemble_blocks(x)
      x = [self.convs[i](feature) for i, feature in enumerate(x)]
      out = self.fusion_blocks[0](x[-1])
      for i in range(1, len(self.fusion_blocks)):
          out = self.fusion_blocks[i](out, x[-(i + 1)])
      out = self.project(out)
      out = self.depth_pred(out)
      return out
  
imports: import copy
  from functools import partial
  import math
  import warnings
  import torch
  import torch.nn
  from .ops import resize
label_desc: Use image node_id 2 for calling the DPTHead obj's underlying member method code with example usage: obj.forward(inputs, img_metas) and returns: out