class_docstr: Basic prediction head that applies a dropout and convolution (without normalization and activation) to the
  feature map with the smallest stride and resize the predicted feature map to the input image size.
docstr: :param feature_maps: Input features (from encoder, decoder or neck, etc.)
  :param output_size:  The desired output size (rows, cols) of the output feature map.
  :return:
docstr_tok: [":","param","feature_maps",":","Input","features","(","from","encoder",",","decoder","or","neck",",","etc",".",")",":","param","output_size",":","The","desired","output","size","(","rows",",","cols",")","of","the","output","feature","map",".",":","return",":"]
code: def forward(
      self,
      feature_maps: List[Tensor],
      output_size: Union[Tuple[int, int], torch.Size],
  ) -> Union[
      Tensor, Tuple[Tensor, ...], List[Tensor], Mapping[str, Tensor]
  ]:
      """
  
      :param feature_maps: Input features (from encoder, decoder or neck, etc.)
      :param output_size:  The desired output size (rows, cols) of the output feature map.
      :return:
      """
      x = feature_maps[self.target_feature_map_index]
      x = self.dropout(x)
      x = self.final(x)
  
      output = torch.nn.functional.interpolate(
          x,
          size=output_size,
          mode=self.interpolation_mode,
          align_corners=self.interpolation_align_corners,
      )
  
      if self.output_name is not None:
          return {self.output_name: output}
      else:
          return output
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/heads/resize.py
imports: from typing import Optional, List, Union, Mapping, Tuple
  import torch.jit
  from torch import nn, Tensor
  from pytorch_toolbelt.modules.interfaces import AbstractHead, FeatureMapsSpecification
label_desc: Use image node_id 2 for calling the ResizeHead obj's underlying member method code with example usage: obj.forward(feature_maps, output_size) and returns: dict, output