# Path: init2winit/dataset_lib/data_utils.py
# def iterator_as_numpy(iterator):
# def image_iterator(data,
#                    rescale,
#                    output_shape,
#                    is_one_hot,
#                    autoencoder,
#                    shuffle_rng=None,
#                    augment_fn=None,
#                    include_example_keys=False):
# def maybe_pad_batch(batch,
#                     desired_batch_size,
#                     data_format=None,
#                     mask_key=None):
#   def zero_pad(ar, pad_axis):
# def shard(batch, n_devices=None):
#   def _shard_array(array):
# def tf_to_numpy(tfds_data):
# 
# Path: init2winit/dataset_lib/data_utils.py
# def iterator_as_numpy(iterator):
# def image_iterator(data,
#                    rescale,
#                    output_shape,
#                    is_one_hot,
#                    autoencoder,
#                    shuffle_rng=None,
#                    augment_fn=None,
#                    include_example_keys=False):
# def maybe_pad_batch(batch,
#                     desired_batch_size,
#                     data_format=None,
#                     mask_key=None):
#   def zero_pad(ar, pad_axis):
# def shard(batch, n_devices=None):
#   def _shard_array(array):
# def tf_to_numpy(tfds_data):


import itertools
import jax
import jraph
import numpy as np
import tensorflow_datasets as tfds
from init2winit.dataset_lib import data_utils
from init2winit.dataset_lib.data_utils import Dataset
from ml_collections.config_dict import config_dict
      'train',
      should_shuffle=True,
      shuffle_seed=shuffle_rng_eval_train,
      shuffle_buffer_size=shuffle_buffer_size)
  valid_ds = _load_dataset('validation')
  test_ds = _load_dataset('test')

  def train_iterator_fn():
    return _get_batch_iterator(
        iter(train_ds), batch_size, hps.max_nodes_multiplier,
        hps.max_edges_multiplier)

  def eval_train_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(eval_train_ds), batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)

  def valid_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(valid_ds), eval_batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)

  def test_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(test_ds), eval_batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)


# Path: init2winit/dataset_lib/data_utils.py
# def iterator_as_numpy(iterator):
# def image_iterator(data,
#                    rescale,
#                    output_shape,
#                    is_one_hot,
#                    autoencoder,
#                    shuffle_rng=None,
#                    augment_fn=None,
#                    include_example_keys=False):
# def maybe_pad_batch(batch,
#                     desired_batch_size,
#                     data_format=None,
#                     mask_key=None):
#   def zero_pad(ar, pad_axis):
# def shard(batch, n_devices=None):
#   def _shard_array(array):
# def tf_to_numpy(tfds_data):
# 
# Path: init2winit/dataset_lib/data_utils.py
# def iterator_as_numpy(iterator):
# def image_iterator(data,
#                    rescale,
#                    output_shape,
#                    is_one_hot,
#                    autoencoder,
#                    shuffle_rng=None,
#                    augment_fn=None,
#                    include_example_keys=False):
# def maybe_pad_batch(batch,
#                     desired_batch_size,
#                     data_format=None,
#                     mask_key=None):
#   def zero_pad(ar, pad_axis):
# def shard(batch, n_devices=None):
#   def _shard_array(array):
# def tf_to_numpy(tfds_data):


# Path: init2winit/dataset_lib/ogbg_molpcba.py
import itertools
import jax
import jraph
import numpy as np
import tensorflow_datasets as tfds
from init2winit.dataset_lib import data_utils
from init2winit.dataset_lib.data_utils import Dataset
from ml_collections.config_dict import config_dict

      'train',
      should_shuffle=True,
      shuffle_seed=shuffle_rng_eval_train,
      shuffle_buffer_size=shuffle_buffer_size)
  valid_ds = _load_dataset('validation')
  test_ds = _load_dataset('test')

  def train_iterator_fn():
    return _get_batch_iterator(
        iter(train_ds), batch_size, hps.max_nodes_multiplier,
        hps.max_edges_multiplier)

  def eval_train_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(eval_train_ds), batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)

  def valid_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(valid_ds), eval_batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)

  def test_epoch(num_batches=None):
    return itertools.islice(
        _get_batch_iterator(
            iter(test_ds), eval_batch_size, hps.max_nodes_multiplier,
            hps.max_edges_multiplier), num_batches)


  return Dataset(train_iterator_fn, eval_train_epoch, valid_epoch, test_epoch)