code: def pad_to_fixed_size(data, pad_value, output_shape):
      """Pad data to a fixed length at the first dimension.
  
      Args:
        data: Tensor to be padded to output_shape.
        pad_value: A constant value assigned to the paddings.
        output_shape: The output shape of a 2D tensor.
      Returns:
        The Padded tensor with output_shape [max_instances_per_image, dimension].
      """
      max_instances_per_image = output_shape[0]
      dimension = output_shape[1]
      data = tf.reshape(data, [-1, dimension])
      num_instances = tf.shape(data)[0]
      msg = "ERROR: please increase config.max_instances_per_image"
      with tf.control_dependencies(
          [
              tf.assert_less(
                  num_instances, max_instances_per_image, message=msg
              )
          ]
      ):
          pad_length = max_instances_per_image - num_instances
      paddings = pad_value * tf.ones([pad_length, dimension])
      padded_data = tf.concat([data, paddings], axis=0)
      padded_data = tf.reshape(padded_data, output_shape)
      return padded_data
  
imports: from absl import logging
  import tensorflow
  import utils
  from pipeline import anchors
  from .None import preprocessor
  from .None import tf_example_decoder
label_desc: Use image node_id 1 for calling a global function with example usage: pad_to_fixed_size(data, pad_value, output_shape) and returns: padded_data