class_docstr: A conv block that bundles conv/norm/activation layers.
  
  This block simplifies the usage of convolution layers, which are commonly
  used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
  It is based upon three build methods: `build_conv_layer()`,
  `build_norm_layer()` and `build_activation_layer()`.
  
  Besides, we add some additional features in this module.
  1. Automatically set `bias` of the conv layer.
  2. Spectral norm is supported.
  3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
  supports zero and circular padding, and we add "reflect" padding mode.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
          Same as that in ``nn._ConvNd``.
      out_channels (int): Number of channels produced by the convolution.
          Same as that in ``nn._ConvNd``.
      kernel_size (int | tuple[int]): Size of the convolving kernel.
          Same as that in ``nn._ConvNd``.
      stride (int | tuple[int]): Stride of the convolution.
          Same as that in ``nn._ConvNd``.
      padding (int | tuple[int]): Zero-padding added to both sides of
          the input. Same as that in ``nn._ConvNd``.
      dilation (int | tuple[int]): Spacing between kernel elements.
          Same as that in ``nn._ConvNd``.
      groups (int): Number of blocked connections from input channels to
          output channels. Same as that in ``nn._ConvNd``.
      bias (bool | str): If specified as `auto`, it will be decided by the
          norm_layer. Bias will be set as True if `norm_layer` is None, otherwise
          False. Default: "auto".
      conv_layer (nn.Module): Convolution layer. Default: None,
          which means using conv2d.
      norm_layer (nn.Module): Normalization layer. Default: None.
      act_layer (nn.Module): Activation layer. Default: nn.ReLU.
      inplace (bool): Whether to use inplace mode for activation.
          Default: True.
      with_spectral_norm (bool): Whether use spectral norm in conv module.
          Default: False.
      padding_mode (str): If the `padding_mode` has not been supported by
          current `Conv2d` in PyTorch, we will use our own padding layer
          instead. Currently, we support ['zeros', 'circular'] with official
          implementation and ['reflect'] with our own implementation.
          Default: 'zeros'.
      order (tuple[str]): The order of conv/norm/activation layers. It is a
          sequence of "conv", "norm" and "act". Common examples are
          ("conv", "norm", "act") and ("act", "conv", "norm").
          Default: ('conv', 'norm', 'act').
code: def norm(self):
      if self.norm_name:
          return getattr(self, self.norm_name)
      else:
          return None
  
imports: import copy
  from functools import partial
  import math
  import warnings
  import torch
  import torch.nn
  from .ops import resize
label_desc: Use image node_id 2 for calling the ConvModule obj's underlying member method code with example usage: obj.norm() and returns: getattr, None