class_docstr: Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.
  
  Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
      https://arxiv.org/abs/1903.10520v2
class_docstr_tok: ["Conv2d","with","Weight","Standardization",".","TF","compatible","SAME","padding",".","Used","for","ViT","Hybrid","model",".","Paper",":","`","Micro-Batch","Training","with","Batch-Channel","Normalization","and","Weight","Standardization","`","-","https",":","\/\/arxiv.org\/abs\/1903.10520v2"]
code: def __init__(
      self,
      in_channel,
      out_channels,
      kernel_size,
      stride=1,
      padding="SAME",
      dilation=1,
      groups=1,
      bias=False,
      eps=1e-6,
  ):
      padding, is_dynamic = get_padding_value(
          padding, kernel_size, stride=stride, dilation=dilation
      )
      super().__init__(
          in_channel,
          out_channels,
          kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
          bias=bias,
      )
      self.same_pad = is_dynamic
      self.eps = eps
  
file_path: pytorch-image-models/timm/layers/std_conv.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .padding import get_padding, get_padding_value, pad_same
label_desc: Use image node_id 1 to create a new StdConv2dSame object from inherited base classes: nn with example: obj = StdConv2dSame(in_channel, out_channels, kernel_size, stride, padding, dilation, groups, bias, eps)