Another fix

  y = df[sorted_tasks].values

  y = np.reshape(y, (n_samples, n_tasks))

  w = np.ones((n_samples, n_tasks))

  missing = np.ones_like(y).astype(int)

  missing = np.zeros_like(y).astype(int)

  tensors = []

  print("n_samples")

  print(n_samples)

  for ind in range(n_samples):

    print("ind")

    print(ind)

    datapoint = df.iloc[ind]

    feature_list = []

    for feature_type in feature_types:

    # TODO(rbharath): Total hack. Fix before merge!!!

    try:

      features = np.squeeze(np.concatenate(feature_list))

      for ind, val in enumerate(features):

        if features[ind] == "":

          features[ind] = 0.

      for feature_ind, val in enumerate(features):

        if features[feature_ind] == "":

          features[feature_ind] = 0.

      features = features.astype(float)

      n_features = features.shape[0]

    except ValueError:

      missing[ind] = 0

      missing[ind, :] = 1

      continue

    for task in range(n_tasks):

      if y[ind, task] == "":

        missing[ind, task] = 1

    tensors.append(features)

  x = np.stack(tensors)

  sorted_ids = df["mol_id"]

  # Set missing data to have weight zero

  y[missing] = 0.

  w[missing] = 0.

  # TODO(rbharath): There's a better way to do this with numpy indexing

  for ind in range(n_samples):

    for task in range(n_tasks):

      if missing[ind, task]:

        y[ind, task] = 0.

        w[ind, task] = 0.

  print("x")

  print(x)

  print("w")

  print(w)

  print("missing")

  print(missing)

  print("y")

  print(y)

  return sorted_ids, x.astype(float), y.astype(float), w.astype(float)

def compute_mean_and_std(df):