import tensorflow as tf

    filter_h, filter_w, num_channels_out = filter_dims
    stride_h, stride_w = stride_dims

    with tf.variable_scope(scope):
        conv_weight = tf.Variable(
            tf.truncated_normal([filter_h, filter_w, num_channels_in, num_channels_out], stddev=0.1, dtype=tf.float32))
        conv_bias = tf.Variable(tf.zeros([num_channels_out], dtype=tf.float32))
        map = tf.nn.conv2d(input, conv_weight, strides=[1, stride_h, stride_w, 1], padding=padding, dilations=dilation)

        if bias is True:
