class_docstr: The Convolutional 3D network (C3D).
  Learning Spatiotemporal Features with 3D Convolutional Networks.
  ICCV, 2015. https://arxiv.org/abs/1412.0767
  
  Parameters
  ----------
  nclass : int
      Number of classes in the training dataset.
  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.
  feat_ext : bool.
      Whether to extract features before dense classification layer or
      do a complete forward pass.
  dropout_ratio : float
      Dropout value used in the dropout layers after dense layers to avoid overfitting.
  init_std : float
      Default standard deviation value for initializing dense layers.
  ctx : str
      Context, default CPU. The context in which to load the pretrained weights.
docstr: Hybrid forward of C3D net
code: def hybrid_forward(self, F, x):
      """Hybrid forward of C3D net"""
      x = self.relu(self.conv1(x))
      x = self.pool1(x)
  
      x = self.relu(self.conv2(x))
      x = self.pool2(x)
  
      x = self.relu(self.conv3a(x))
      x = self.relu(self.conv3b(x))
      x = self.pool3(x)
  
      x = self.relu(self.conv4a(x))
      x = self.relu(self.conv4b(x))
      x = self.pool4(x)
  
      x = self.relu(self.conv5a(x))
      x = self.relu(self.conv5b(x))
      x = self.pool5(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.relu(self.fc6(x))
      x = self.dropout(x)
  
      if self.feat_ext:
          return x
  
      x = self.relu(self.fc7(x))
      x = self.dropout(x)
      x = self.fc8(x)
      return x
  
file_path: gluon-cv/gluoncv/model_zoo/action_recognition/c3d.py
imports: from mxnet import init
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 2 for calling the C3D obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x, x