class_docstr: Basic conv bn layer.
code: def __init__(
      self,
      num_channels: int,
      filter_size: int,
      num_filters: int,
      stride: int,
      padding: int,
      num_groups: int = 1,
      name: str = None,
  ):
      super(ConvBNLayer, self).__init__()
  
      self._conv = Conv2d(
          in_channels=num_channels,
          out_channels=num_filters,
          kernel_size=filter_size,
          stride=stride,
          padding=padding,
          groups=num_groups,
          weight_attr=ParamAttr(name=name + "_weights"),
          bias_attr=False,
      )
  
      self._batch_norm = BatchNorm(
          num_filters,
          param_attr=ParamAttr(name=name + "_bn_scale"),
          bias_attr=ParamAttr(name=name + "_bn_offset"),
          moving_mean_name=name + "_bn_mean",
          moving_variance_name=name + "_bn_variance",
      )
  
imports: import os
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
  from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(num_channels, filter_size, num_filters, stride, padding, num_groups, name)