class_docstr: RCNN network.
  
  Parameters
  ----------
  features : gluon.HybridBlock
      Base feature extractor before feature pooling layer.
  top_features : gluon.HybridBlock
      Tail feature extractor after feature pooling layer.
  classes : iterable of str
      Names of categories, its length is ``num_class``.
  box_features : gluon.HybridBlock
      feature head for transforming roi output for box prediction.
  short : int
      Input image short side size.
  max_size : int
      Maximum size of input image long side.
  train_patterns : str
      Matching pattern for trainable parameters.
  nms_thresh : float
      Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
  nms_topk : int
      Apply NMS to top k detection results, use -1 to disable so that every Detection
       result is used in NMS.
  roi_mode : str
      ROI pooling mode. Currently support 'pool' and 'align'.
  roi_size : tuple of int, length 2
      (height, width) of the ROI region.
  strides : int/tuple of ints
      Stride(s) of network features. Tuple for FPN.
  clip: float
      Clip bounding box target to this value.
  
  Attributes
  ----------
  classes : iterable of str
      Names of categories, its length is ``num_class``.
  num_class : int
      Number of positive categories.
  short : int
      Input image short side size.
  max_size : int
      Maximum size of input image long side.
  train_patterns : str
      Matching pattern for trainable parameters.
  nms_thresh : float
      Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
  nms_topk : int
      Apply NMS to top k detection results, use -1 to disable so that every Detection
       result is used in NMS.
  minimal_opset : bool
      We sometimes add special operators to accelerate training/inference, however, for exporting
      to third party compilers we want to utilize most widely used operators.
      If `minimal_opset` is `True`, the network will use a minimal set of operators good
      for e.g., `TVM`.
docstr: Not implemented yet.
docstr_tok: ["Not","implemented","yet","."]
code: def hybrid_forward(self, F, x, width, height):
      """Not implemented yet."""
      raise NotImplementedError
  
file_path: gluon-cv/gluoncv/model_zoo/rcnn/rcnn.py
imports: from __future__ import absolute_import
  import warnings
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from ...nn.coder import NormalizedBoxCenterDecoder, MultiPerClassDecoder
  from ...nn.feature import FPNFeatureExpander
label_desc: Use image node_id 5 for calling the RCNN obj's underlying member method code with example usage: obj.hybrid_forward(F, x, width, height) without return types