class_docstr: Base class for BaseDecodeHead.
  
  Args:
      in_channels (List): Input channels.
      channels (int): Channels after modules, before conv_depth.
      conv_layer (nn.Module): Conv layers. Default: None.
      act_layer (nn.Module): Activation layers. Default: nn.ReLU.
      loss_decode (dict): Config of decode loss.
          Default: ().
      sampler (dict|None): The config of depth map sampler.
          Default: None.
      align_corners (bool): align_corners argument of F.interpolate.
          Default: False.
      min_depth (int): Min depth in dataset setting.
          Default: 1e-3.
      max_depth (int): Max depth in dataset setting.
          Default: None.
      norm_layer (dict|None): Norm layers.
          Default: None.
      classify (bool): Whether predict depth in a cls.-reg. manner.
          Default: False.
      n_bins (int): The number of bins used in cls. step.
          Default: 256.
      bins_strategy (str): The discrete strategy used in cls. step.
          Default: 'UD'.
      norm_strategy (str): The norm strategy on cls. probability
          distribution. Default: 'linear'
      scale_up (str): Whether predict depth in a scale-up manner.
          Default: False.
docstr: Placeholder of forward function.
docstr_tok: ["Placeholder","of","forward","function","."]
code: def forward(self, inputs, img_metas):
      """Placeholder of forward function."""
      pass
  
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 DepthBaseDecodeHead obj's underlying member method code with example usage: obj.forward(inputs, img_metas) without return types