code: def conv2d_same(
      x,
      weight: torch.Tensor,
      bias: Optional[torch.Tensor] = None,
      stride: Tuple[int, int] = (1, 1),
      padding: Tuple[int, int] = (0, 0),
      dilation: Tuple[int, int] = (1, 1),
      groups: int = 1,
  ):
      x = pad_same(x, weight.shape[-2:], stride, dilation)
      return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
  
file_path: lavis/lavis/models/timesformer/conv2d_same.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Tuple, Optional
  import math
  from typing import List, Tuple
  from .vit_utils import is_static_pad, get_padding
label_desc: Use image node_id 4 for calling a global function with example usage: conv2d_same(x, weight, bias, stride, padding, dilation, groups) and returns: F