class_docstr: Inline Conv2D for GroupNorm module
class_docstr_tok: ["Inline","Conv2D","for","GroupNorm","module"]
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      padding="same",
      stride=1,
      dilation=1,
      groups=1,
  ):
      super(Conv2D, self).__init__()
  
      assert isinstance(
          kernel_size, (tuple, int)
      ), "Allowed kernel type [int or tuple], not {}".format(
          type(kernel_size)
      )
      assert (
          padding == "same"
      ), "Allowed padding type {}, not {}".format("same", padding)
  
      self.kernel_size = kernel_size
      if isinstance(kernel_size, tuple):
          self.h_kernel = kernel_size[0]
          self.w_kernel = kernel_size[1]
      else:
          self.h_kernel = kernel_size
          self.w_kernel = kernel_size
  
      self.padding = padding
      self.stride = stride
      self.dilation = dilation
      self.groups = groups
      self.conv = nn.Conv2d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=self.stride,
          dilation=self.dilation,
          groups=self.groups,
      )
  
file_path: gluon-cv/gluoncv/torch/nn/group_norm.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, Parameter
  from torch.nn import init
label_desc: Use image node_id 1 to create a new Conv2D object from inherited base classes: nn with example: obj = Conv2D(in_channels, out_channels, kernel_size, padding, stride, dilation, groups)