code: def load_gutenberg_spacy_ner_monitoring():
      print("Loading Gutenberg_spacy-ner-monitoring dataset")
  
      # Load dataset from the hub
      dataset = load_dataset(
          "argilla/gutenberg_spacy-ner", split="train"
      )
  
      # Read in dataset, assuming it's a dataset for token classification
      dataset_rg = rg.read_datasets(dataset, task="TokenClassification")
  
      # Define labeling schema to avoid UI user modification
      labels = {
          "CARDINAL",
          "DATE",
          "EVENT",
          "FAC",
          "GPE",
          "LANGUAGE",
          "LAW",
          "LOC",
          "MONEY",
          "NORP",
          "ORDINAL",
          "ORG",
          "PERCENT",
          "PERSON",
          "PRODUCT",
          "QUANTITY",
          "TIME",
          "WORK_OF_ART",
      }
      settings = rg.TokenClassificationSettings(label_schema=labels)
      rg.configure_dataset(
          name="gutenberg_spacy-ner-monitoring", settings=settings
      )
  
      # Log the dataset
      rg.log(
          dataset_rg,
          "gutenberg_spacy-ner-monitoring",
          tags={
              "description": "A dataset containing text from books with predictions from two spaCy NER pre-trained "
              "models. "
          },
      )
  
file_path: argilla/docker/scripts/load_data.py
imports: import sys
  import time
  from typing import Union
  import argilla
  import pandas
  import requests
  from argilla.labeling.text_classification import Rule, add_rules
  from datasets import load_dataset
label_desc: Use image node_id 5 for calling the LoadDatasets obj's underlying member method code with example usage: obj.load_gutenberg_spacy_ner_monitoring() without return types