docstr: kwargs:
      max_detections (int), return_tensors {"np", "pt", None}, padding {None,
      "max_detections"}, pad_value (int), location = {"cuda", "cpu"}
docstr_tok: ["kwargs",":","max_detections","(","int",")",",","return_tensors","{","``","np","''",",","``","pt","''",",","None","}",",","padding","{","None",",","``","max_detections","''","}",",","pad_value","(","int",")",",","location","=","{","``","cuda","''",",","``","cpu","''","}"]
code: def forward(
      self,
      images: torch.Tensor,
      image_shapes: torch.Tensor,
      gt_boxes: torch.Tensor = None,
      proposals: torch.Tensor = None,
      scales_yx: torch.Tensor = None,
      **kwargs,
  ):
      """
      kwargs:
          max_detections (int), return_tensors {"np", "pt", None}, padding {None,
          "max_detections"}, pad_value (int), location = {"cuda", "cpu"}
      """
      if self.training:
          raise NotImplementedError()
      return self.inference(
          images=images,
          image_shapes=image_shapes,
          gt_boxes=gt_boxes,
          proposals=proposals,
          scales_yx=scales_yx,
          **kwargs,
      )
  
file_path: mmf/mmf/models/frcnn.py
imports: from typing import List
  import omegaconf
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 5 for calling the GeneralizedRCNN obj's underlying member method code with example usage: obj.forward(images, image_shapes, gt_boxes, proposals, scales_yx) and returns: self