class_docstr: ResNet models for video action recognition
  Deep Residual Learning for Image Recognition, CVPR 2016
  https://arxiv.org/abs/1512.03385
  
  Parameters
  ----------
  depth : int, default is 50.
      Depth of ResNet, from {18, 34, 50, 101, 152}.
  nclass : int
      Number of classes in the training dataset.
  pretrained_base : bool or str, optional, default is True.
      Load pretrained base network, the extra layers are randomized. Note that
      if pretrained is `True`, this has no effect.
  partial_bn : bool, default False.
      Freeze all batch normalization layers during training except the first layer.
  dropout_ratio : float, default is 0.5.
      The dropout rate of a dropout layer.
      The larger the value, the more strength to prevent overfitting.
  init_std : float, default is 0.001.
      Standard deviation value when initialize the dense layers.
  num_segments : int, default is 1.
      Number of segments used to evenly divide a video.
  num_crop : int, default is 1.
      Number of crops used during evaluation, choices are 1, 3 or 10.
  
  Input: a single video frame or N images from N segments when num_segments > 1
  Output: a single predicted action label
code: def hybrid_forward(self, F, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
  
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
  
      x = self.avgpool(x)
      x = self.flat(x)
      x = self.drop(x)
  
      # segmental consensus
      x = F.reshape(
          x,
          shape=(-1, self.num_segments * self.num_crop, self.feat_dim),
      )
      x = F.mean(x, axis=1)
  
      x = self.output(x)
      return x
  
imports: import mxnet
  from mxnet import init
  from mxnet.gluon import nn
  from mxnet.gluon.nn import HybridBlock
  from ..resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1b, resnet101_v1b, resnet152_v1b
label_desc: Use image node_id 2 for calling the ActionRecResNetV1b obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x