class_docstr: CenterPivot 4D conv
  
  Args:
      in_channels (`int`):
          Number of input channels.
      out_channels (`int`):
          Number of output channels.
      kernel_size (`list or tuple`):
          Numbers for kernel size.
      stride (`list or tuple`):
          Numbers for stride.
      padding (`list or tuple`):
          Numbers for padding.
      bias (`bool`):
          Whether to add bias for conv layer.
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      bias=True,
  ):
      super().__init__()
  
      self.conv1 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size[:2],
          stride=stride[:2],
          bias=bias,
          padding=padding[:2],
      )
      self.conv2 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size[2:],
          stride=stride[2:],
          bias=bias,
          padding=padding[2:],
      )
  
      self.stride34 = stride[2:]
      self.kernel_size = kernel_size
      self.stride = stride
      self.padding = padding
      self.idx_initialized = False
      self.idx_initialized_2 = False
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new CenterPivotConv4d object from inherited base classes: nn with example: obj = CenterPivotConv4d(in_channels, out_channels, kernel_size, stride, padding, bias)