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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 patch_discriminator(x, filters=64, filter_size=5, n=4, name="patch_discrim"):
 """Patch descriminator."""

  with tf.variable_scope(name):
    x_shape = shape_list(x)
    spatial_dims = [x_shape[1] // 4, x_shape[2] // 4]
    x = tf.random_crop(x, [x_shape[0]] + spatial_dims + [x_shape[3]])
    for i in range(n):
      x = general_conv(
          x=x,
          num_filters=filters * 2**i,
          filter_size=filter_size,
          stride=2 if i != n - 1 else 1,
          stddev=0.02,
          padding="SAME",
          name="c%d" % i,
          do_norm="instance" if i != 0 else False,
          do_relu=i != n - 1,
          relufactor=0.2)
    x = tf.reduce_mean(x, [1, 2])
    return x