class_docstr: 3D convolutions without temporal dimension for MCx (1x3x3 kernel)
code: def __init__(
      self, in_planes, out_planes, midplanes=None, stride=1, padding=1
  ):
      super(Conv3DNoTemporal, self).__init__(
          in_channels=in_planes,
          out_channels=out_planes,
          kernel_size=(1, 3, 3),
          stride=(1, stride, stride),
          padding=(0, padding, padding),
          bias=False,
      )
  
imports: import torch.nn
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 to create a new Conv3DNoTemporal object from inherited base classes: nn with example: obj = Conv3DNoTemporal(in_planes, out_planes, midplanes, stride, padding)