code: def conv2d_bn(
      x,
      filters,
      kernel_size,
      strides=1,
      padding="same",
      activation="relu",
      use_bias=False,
      name=None,
  ):
      """Utility function to apply conv + BN.
      # Arguments
          x: input tensor.
          filters: filters in `Conv2D`.
          kernel_size: kernel size as in `Conv2D`.
          strides: strides in `Conv2D`.
          padding: padding mode in `Conv2D`.
          activation: activation in `Conv2D`.
          use_bias: whether to use a bias in `Conv2D`.
          name: name of the ops; will become `name + '_ac'` for the activation
              and `name + '_bn'` for the batch norm layer.
      # Returns
          Output tensor after applying `Conv2D` and `BatchNormalization`.
      """
      x = layers.Conv2D(
          filters,
          kernel_size,
          strides=strides,
          padding=padding,
          use_bias=use_bias,
          name=name,
      )(x)
      if not use_bias:
          bn_axis = (
              1
              if backend.image_data_format() == "channels_first"
              else 3
          )
          bn_name = None if name is None else name + "_bn"
          x = layers.BatchNormalization(
              axis=bn_axis, scale=False, name=bn_name
          )(x)
      if activation is not None:
          ac_name = None if name is None else name + "_ac"
          x = layers.Activation(activation, name=ac_name)(x)
      return x
  
file_path: segmentation_models/segmentation_models/backbones/inception_resnet_v2.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 2 for calling a global function with example usage: conv2d_bn(x, filters, kernel_size, strides, padding, activation, use_bias, name) and returns: x