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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def single_discriminator(x, filters=128, kernel_size=8, strides=4, pure_mean=False):
 """A simple single-layer convolutional discriminator."""

  with tf.variable_scope("discriminator"):
    net = layers().Conv2D(
        filters, kernel_size, strides=strides, padding="SAME", name="conv1")(x)
    if pure_mean:
      net = tf.reduce_mean(net, [1, 2])
    else:
      net = mean_with_attention(net, "mean_with_attention")
    return net