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.
code: def log_images(self, img_path, depth_pred, depth_gt, img_meta):
      import numpy as np
  
      show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
      show_img = show_img.numpy().astype(np.float32)
      show_img = _imdenormalize(
          show_img,
          img_meta["img_norm_cfg"]["mean"],
          img_meta["img_norm_cfg"]["std"],
          img_meta["img_norm_cfg"]["to_rgb"],
      )
      show_img = np.clip(show_img, 0, 255)
      show_img = show_img.astype(np.uint8)
      show_img = show_img[:, :, ::-1]
      show_img = show_img.transpose(0, 2, 1)
      show_img = show_img.transpose(1, 0, 2)
  
      depth_pred = depth_pred / torch.max(depth_pred)
      depth_gt = depth_gt / torch.max(depth_gt)
  
      depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
      depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
  
      return {
          "img_rgb": show_img,
          "img_depth_pred": depth_pred_color,
          "img_depth_gt": depth_gt_color,
      }
  
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 7 for calling the DepthBaseDecodeHead obj's underlying member method code with example usage: obj.log_images(img_path, depth_pred, depth_gt, img_meta) and returns: dict