A 3x3 convolution mapping x to a standard normal distribution at init.

  Args:
    name: variable scope.
    x: 4-D Tensor.
    output_channels: number of channels of the mean and std.

def single_conv_dist(name, x, output_channels=None):
  """A 3x3 convolution mapping x to a standard normal distribution at init.

  Args:
    name: variable scope.
    x: 4-D Tensor.
    output_channels: number of channels of the mean and std.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x_shape = common_layers.shape_list(x)
    if output_channels is None:
      output_channels = x_shape[-1]
    mean_log_scale = conv("conv2d", x, output_channels=2*output_channels,
                          conv_init="zeros", apply_actnorm=False)
    mean = mean_log_scale[:, :, :, 0::2]
    log_scale = mean_log_scale[:, :, :, 1::2]
    return tf.distributions.Normal(mean, tf.exp(log_scale))