code: def conv2d_bn(
      x,
      filters,
      num_row,
      num_col,
      padding="same",
      strides=(1, 1),
      name=None,
  ):
      """Utility function to apply conv + BN.
      # Arguments
          x: input tensor.
          filters: filters in `Conv2D`.
          num_row: height of the convolution kernel.
          num_col: width of the convolution kernel.
          padding: padding mode in `Conv2D`.
          strides: strides in `Conv2D`.
          name: name of the ops; will become `name + '_conv'`
              for the convolution and `name + '_bn'` for the
              batch norm layer.
      # Returns
          Output tensor after applying `Conv2D` and `BatchNormalization`.
      """
      if name is not None:
          bn_name = name + "_bn"
          conv_name = name + "_conv"
      else:
          bn_name = None
          conv_name = None
      if backend.image_data_format() == "channels_first":
          bn_axis = 1
      else:
          bn_axis = 3
      x = layers.Conv2D(
          filters,
          (num_row, num_col),
          strides=strides,
          padding=padding,
          use_bias=False,
          name=conv_name,
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis, scale=False, name=bn_name
      )(x)
      x = layers.Activation("relu", name=name)(x)
      return x
  
file_path: segmentation_models/segmentation_models/backbones/inception_v3.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from keras_applications import imagenet_utils
  from keras_applications import get_submodules_from_kwargs
label_desc: Use image node_id 1 for calling a global function with example usage: conv2d_bn(x, filters, num_row, num_col, padding, strides, name) and returns: x