docstr: Shape of video model input can vary in the following cases
  - At training stage, input are video frame croppings of fixed size.
  - At test stage, input are original video frames to support Fully Convolutional
      evaluation and its size can vary video by video
docstr_tok: ["Shape","of","video","model","input","can","vary","in","the","following","cases","-","At","training","stage",",","input","are","video","frame","croppings","of","fixed","size",".","-","At","test","stage",",","input","are","original","video","frames","to","support","Fully","Convolutional","evaluation","and","its","size","can","vary","video","by","video"]
code: def input_shape(self):
      """
      Shape of video model input can vary in the following cases
      - At training stage, input are video frame croppings of fixed size.
      - At test stage, input are original video frames to support Fully Convolutional
          evaluation and its size can vary video by video
      """
      # Input shape is used by tensorboard hook. We put the input shape at
      # training stage for profiling and visualization purpose.
      return (
          self.input_planes,
          self.frames_per_clip,
          self.clip_crop_size,
          self.clip_crop_size,
      )
  
imports: import logging
  from typing import Any, Dict, Sequence
  import torch
  import torch.nn
  from classy_vision.generic.util import is_pos_int, is_pos_int_list
  from .None import register_model
  from .classy_model import ClassyModel
  from .resnext3d_stage import ResStage
  from .resnext3d_stem import R2Plus1DStem, ResNeXt3DStem
label_desc: Use image node_id 6 for calling the ResNeXt3DBase obj's underlying member method code with example usage: obj.input_shape() and returns: self,  self,  self,  self