class_docstr: 2D Convolutions like TensorFlow, for a dynamic image size.
  The padding is operated in forward function by calculating dynamically.
class_docstr_tok: ["2D","Convolutions","like","TensorFlow",",","for","a","dynamic","image","size",".","The","padding","is","operated","in","forward","function","by","calculating","dynamically","."]
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      dilation=1,
      groups=1,
      bias=True,
  ):
      super().__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          0,
          dilation,
          groups,
          bias,
      )
      self.stride = (
          self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
      )
  
imports: import re
  import math
  import collections
  from functools import partial
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.utils import model_zoo
label_desc: Use image node_id 1 to create a new Conv2dDynamicSamePadding object from inherited base classes: nn with example: obj = Conv2dDynamicSamePadding(in_channels, out_channels, kernel_size, stride, dilation, groups, bias)