class_docstr: Base class for segmentation heads
class_docstr_tok: ["Base","class","for","segmentation","heads"]
code: def forward(
      self, enc_out: Dict, *args, **kwargs
  ) -> Tensor or Tuple[Tensor]:
      out = self.forward_seg_head(enc_out=enc_out)
  
      if self.upsample_seg_out is not None:
          # resize the mask based on given size
          mask_size = kwargs.get("orig_size", None)
          if mask_size is not None:
              self.upsample_seg_out.scale_factor = None
              self.upsample_seg_out.size = mask_size
  
          out = self.upsample_seg_out(out)
  
      if self.aux_head is not None and self.training:
          aux_out = self.forward_aux_head(enc_out=enc_out)
          return out, aux_out
      return out
  
file_path: ml-cvnets/cvnets/models/segmentation/heads/base_seg_head.py
imports: import argparse
  from typing import Dict, Optional, Tuple
  from torch import Tensor, nn
  from cvnets.layers import ConvLayer2d, Dropout2d, UpSample
  from cvnets.misc.common import parameter_list
  from cvnets.misc.init_utils import initialize_weights
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel
  from utils import logger
label_desc: Use image node_id 4 for calling the BaseSegHead obj's underlying member method code with example usage: obj.forward(enc_out) and returns: out, out,  aux_out