code: def conv_layer(
      self,
      input,
      num_filters,
      filter_size,
      stride=1,
      groups=1,
      act=None,
  ):
      channels = input.shape[1]
      stdv = (3.0 / (filter_size**2 * channels)) ** 0.5
      param_attr = fluid.param_attr.ParamAttr(
          initializer=fluid.initializer.Uniform(-stdv, stdv)
      )
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=(filter_size - 1) // 2,
          groups=groups,
          act=act,
          param_attr=param_attr,
          bias_attr=False,
      )
      return conv
  
file_path: AdvBox/tutorials/image_classification/googlenet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from builtins import object
  import paddle
  import paddle.fluid
label_desc: Use image node_id 2 for calling the GoogleNet obj's underlying member method code with example usage: obj.conv_layer(input, num_filters, filter_size, stride, groups, act) and returns: conv