from tensorflow.contrib.layers.python.layers import initializers



@add_arg_scope
def convolution(inputs,
                num_outputs,
                kernel_size,
                stride=1,
                padding='SAME',
                data_format=None,
                rate=1,
                activation_fn=nn.relu,
                normalizer_fn=None,
                normalizer_params=None,
                weights_initializer=initializers.xavier_initializer(),
                weights_regularizer=None,
                biases_initializer=init_ops.zeros_initializer(),
                biases_regularizer=None,
                reuse=None,
                variables_collections=None,
                outputs_collections=None,
                trainable=True,
                use_spectral_norm=False,
                is_training=False,
                scope=None,
                conv_dims=None):
    """Adds an N-D convolution followed by an optional batch_norm layer.
