class_docstr: Convolutional Gru unit.
class_docstr_tok: ["Convolutional","Gru","unit","."]
code: def __init__(self, *, input_size, hidden_size, kernel_size, padding):
      super().__init__()
      self.convz = nn.Conv2d(
          hidden_size + input_size,
          hidden_size,
          kernel_size=kernel_size,
          padding=padding,
      )
      self.convr = nn.Conv2d(
          hidden_size + input_size,
          hidden_size,
          kernel_size=kernel_size,
          padding=padding,
      )
      self.convq = nn.Conv2d(
          hidden_size + input_size,
          hidden_size,
          kernel_size=kernel_size,
          padding=padding,
      )
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.instancenorm import InstanceNorm2d
  from torchvision.ops import Conv2dNormActivation
  from ...transforms._presets import OpticalFlow
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._utils import handle_legacy_interface
  from ._utils import grid_sample, make_coords_grid, upsample_flow
label_desc: Use image node_id 1 to create a new ConvGRU object from inherited base classes: nn with example: obj = ConvGRU()