human
!cd /content/nsl && bash neural_structured_learning/examples/preprocess/cora/prep_data.sh

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import threading
from absl import app
from absl import flags
from absl import logging
import attr

import tensorflow as tf
from google.protobuf import text_format

NBR_FEATURE_PREFIX = 'NL_nbr_'
NBR_WEIGHT_SUFFIX = '_weight'
@attr.s
class HParams(object):
  ### dataset parameters
  num_classes = attr.ib(default=7)
  max_seq_length = attr.ib(default=1433)
  ### NGM parameters
  graph_regularization_multiplier = attr.ib(default=0.1)
  num_neighbors = attr.ib(default=1)
  ### model architecture
  num_fc_units = attr.ib(default=[50, 50])
  ### training parameters
  train_epochs = attr.ib(default=10)
  batch_size = attr.ib(default=128)
  dropout_rate = attr.ib(default=0.5)
  ### eval parameters
  eval_steps = attr.ib(default=None)  # Every test instance is evaluated.

hparams = HParams()
NBR_FEATURE_PREFIX = 'NL_nbr_'
NBR_WEIGHT_SUFFIX = '_weight'

def make_dataset(file_path, training, include_nbr_features, hparams):
  """Returns a `tf.data.Dataset` instance based on data in `file_path`."""

  def parse_example(example_proto):
    """Extracts relevant fields from the `example_proto`.
    Args:
      example_proto: An instance of `tf.train.Example`.
    Returns:
      A pair whose first value is a dictionary containing relevant features
      and whose second value contains the ground truth labels.
    """
    # The 'words' feature is a multi-hot, bag-of-words representation of the
    # original raw text. A default value is required for examples that don't
    # have the feature.
    feature_spec = {
        'id':
            tf.io.FixedLenFeature((), tf.string, default_value=''),
        'words':
            tf.io.FixedLenFeature([hparams.max_seq_length],
                                  tf.int64,
                                  default_value=tf.constant(
                                      0,
       ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Prepare dataset and build the package with bazel