code: def __init__(
      self,
      num_channels,
      num_filters,
      filter_size,
      stride=1,
      dilation=1,
      groups=1,
      act=None,
      lr_mult=1.0,
      name=None,
      data_format="NCHW",
  ):
      super(ConvBNLayer, self).__init__()
      conv_stdv = filter_size * filter_size * num_filters
      self._conv = nn.Conv2D(
          in_channels=num_channels,
          out_channels=num_filters,
          kernel_size=filter_size,
          stride=stride,
          padding=(filter_size - 1) // 2,
          dilation=dilation,
          groups=groups,
          weight_attr=ParamAttr(
              learning_rate=lr_mult,
              initializer=Normal(0, math.sqrt(2.0 / conv_stdv)),
          ),
          bias_attr=False,
          data_format=data_format,
      )
  
      self._batch_norm = nn.BatchNorm2D(num_filters)
      self.act = act
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import math
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(num_channels, num_filters, filter_size, stride, dilation, groups, act, lr_mult, name, data_format)