code: def retrieve_out_channels(
      model: nn.Module, size: Tuple[int, int]
  ) -> List[int]:
      """
      This method retrieves the number of output channels of a specific model.
  
      Args:
          model (nn.Module): The model for which we estimate the out_channels.
              It should return a single Tensor or an OrderedDict[Tensor].
          size (Tuple[int, int]): The size (wxh) of the input.
  
      Returns:
          out_channels (List[int]): A list of the output channels of the model.
      """
      in_training = model.training
      model.eval()
  
      with torch.no_grad():
          # Use dummy data to retrieve the feature map sizes to avoid hard-coding their values
          device = next(model.parameters()).device
          tmp_img = torch.zeros((1, 3, size[1], size[0]), device=device)
          features = model(tmp_img)
          if isinstance(features, torch.Tensor):
              features = OrderedDict([("0", features)])
          out_channels = [x.size(1) for x in features.values()]
  
      if in_training:
          model.train()
  
      return out_channels
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import complete_box_iou_loss, distance_box_iou_loss, FrozenBatchNorm2d, generalized_box_iou_loss
label_desc: Use image node_id 3 for calling a global function with example usage: retrieve_out_channels(model, size) and returns: out_channels