def make_prediction_net(num_out_channels, kernel_size=3, num_filters=256, bias_fill=None):
    'Creates a network to predict the given number of output channels.\n\n  This function is intended to make the prediction heads for the CenterNet\n  meta architecture.\n\n  Args:\n    num_out_channels: Number of output channels.\n    kernel_size: The size of the conv kernel in the intermediate layer\n    num_filters: The number of filters in the intermediate conv layer.\n    bias_fill: If not None, is used to initialize the bias in the final conv\n      layer.\n\n  Returns:\n    net: A keras module which when called on an input tensor of size\n      [batch_size, height, width, num_in_channels] returns an output\n      of size [batch_size, height, width, num_out_channels]\n  '
    out_conv = tf.keras.layers.Conv2D(num_out_channels, kernel_size=1)
    if (bias_fill is not None):
        out_conv.bias_initializer = tf.keras.initializers.constant(bias_fill)
    net = tf.keras.Sequential([tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding='same'), tf.keras.layers.ReLU(), out_conv])
    return net
Creates a network to predict the given number of output channels.

This function is intended to make the prediction heads for the CenterNet
meta architecture.

Args:
  num_out_channels: Number of output channels.
  kernel_size: The size of the conv kernel in the intermediate layer
  num_filters: The number of filters in the intermediate conv layer.
  bias_fill: If not None, is used to initialize the bias in the final conv
    layer.

Returns:
  net: A keras module which when called on an input tensor of size
    [batch_size, height, width, num_in_channels] returns an output
    of size [batch_size, height, width, num_out_channels]
def make_prediction_net(num_out_channels, kernel_size=3, num_filters=256, bias_fill=None):
    'Creates a network to predict the given number of output channels.\n\n  This function is intended to make the prediction heads for the CenterNet\n  meta architecture.\n\n  Args:\n    num_out_channels: Number of output channels.\n    kernel_size: The size of the conv kernel in the intermediate layer\n    num_filters: The number of filters in the intermediate conv layer.\n    bias_fill: If not None, is used to initialize the bias in the final conv\n      layer.\n\n  Returns:\n    net: A keras module which when called on an input tensor of size\n      [batch_size, height, width, num_in_channels] returns an output\n      of size [batch_size, height, width, num_out_channels]\n  '
    out_conv = tf.keras.layers.Conv2D(num_out_channels, kernel_size=1)
    if (bias_fill is not None):
        out_conv.bias_initializer = tf.keras.initializers.constant(bias_fill)
    net = tf.keras.Sequential([tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding='same'), tf.keras.layers.ReLU(), out_conv])
    return net