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
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`.
    
segmentation_models/backbones/inception_resnet_v2.py