class_docstr: Tensorflow like 'SAME' convolution wrapper for 2D convolutions.
  
  Args:
      in_channels (`int`):
          Number of channels in the input image.
      out_channels (`int`):
          Number of channels produced by the convolution.
      kernel_size (`Union[int, Tuple]`):
          Size of the convolving kernel.
      stride (`Union[int, Tuple]`):
          Stride of the convolution.
      padding (`Union[int, Tuple, str]`):
          Padding added to all four sides of the input.
      dilation (`int`):
          Spacing between kernel elements.
      groups (`int`):
          Number of blocked connections from input channels to output channels.
      bias (`bool`):
          If True, adds a learnable bias to the output.
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code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: _size_2_t,
      stride: _size_2_t = 1,
      padding: Union[str, _size_2_t] = 0,
      dilation: _size_2_t = 1,
      groups: int = 1,
      bias: bool = True,
  ) -> None:
      super().__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          0,
          dilation,
          groups,
          bias,
      )
      _ = padding
  
file_path: towhee/towhee/models/layers/conv2d_same.py
imports: from typing import Tuple, Optional, Union
  import torch
  from torch import nn
  import torch.nn.functional
  from torch.nn.common_types import _size_2_t
  from towhee.models.layers.padding_functions import pad_same
label_desc: Use image node_id 1 to create a new Conv2dSame object from inherited base classes: nn with example: obj = Conv2dSame(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)