code: def forward(self, x, N_views_xa=1):
      w = self.weight.to(x.dtype) if self.weight is not None else None
      b = self.bias.to(x.dtype) if self.bias is not None else None
      f = (
          self.resample_filter.to(x.dtype)
          if self.resample_filter is not None
          else None
      )
      w_pad = w.shape[-1] // 2 if w is not None else 0
      f_pad = (f.shape[-1] - 1) // 2 if f is not None else 0
  
      if self.fused_resample and self.up and w is not None:
          x = torch.nn.functional.conv_transpose2d(
              x,
              f.mul(4).tile([self.in_channels, 1, 1, 1]),
              groups=self.in_channels,
              stride=2,
              padding=max(f_pad - w_pad, 0),
          )
          x = torch.nn.functional.conv2d(
              x, w, padding=max(w_pad - f_pad, 0)
          )
      elif self.fused_resample and self.down and w is not None:
          x = torch.nn.functional.conv2d(x, w, padding=w_pad + f_pad)
          x = torch.nn.functional.conv2d(
              x,
              f.tile([self.out_channels, 1, 1, 1]),
              groups=self.out_channels,
              stride=2,
          )
      else:
          if self.up:
              x = torch.nn.functional.conv_transpose2d(
                  x,
                  f.mul(4).tile([self.in_channels, 1, 1, 1]),
                  groups=self.in_channels,
                  stride=2,
                  padding=f_pad,
              )
          if self.down:
              x = torch.nn.functional.conv2d(
                  x,
                  f.tile([self.in_channels, 1, 1, 1]),
                  groups=self.in_channels,
                  stride=2,
                  padding=f_pad,
              )
          if w is not None:
              x = torch.nn.functional.conv2d(x, w, padding=w_pad)
      if b is not None:
          x = x.add_(b.reshape(1, -1, 1, 1))
      return x
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 2 for calling the Conv2d obj's underlying member method code with example usage: obj.forward(x, N_views_xa) and returns: x