code: def get_exact_gp_regr_doc(doc):
      """Customizes the net docs to avoid duplication."""
      params_start_idx = doc.find("    Parameters\n    ----------")
      doc = doc[params_start_idx:]
      doc = exact_gp_regr_doc_start + " " + doc
  
      pattern = re.compile(r"(\n\s+)(module .*\n)(\s.+){1,99}")
      start, end = pattern.search(doc).span()
      doc = doc[:start] + exact_gp_regr_module_text + doc[end:]
  
      pattern = re.compile(r"(\n\s+)(criterion .*\n)(\s.+){1,99}")
      start, end = pattern.search(doc).span()
      doc = doc[:start] + exact_gp_regr_criterion_text + doc[end:]
  
      pattern = re.compile(r"(\n\s+)(batch_size .*\n)(\s.+){1,99}")
      start, end = pattern.search(doc).span()
      doc = doc[:start] + exact_gp_regr_batch_size_text + doc[end:]
  
      pattern = re.compile(r"(\n\s+)(train_split .*\n)(\s.+){1,99}")
      start, end = pattern.search(doc).span()
      doc = doc[:start] + gp_regr_train_split_text + doc[end:]
  
      doc = doc + gp_likelihood_attribute_text
  
      return doc
  
imports: import pickle
  import re
  import gpytorch
  import numpy
  import torch
  from skorch.net import NeuralNet
  from skorch.dataset import ValidSplit
  from skorch.dataset import unpack_data
  from skorch.callbacks import EpochScoring
  from skorch.callbacks import EpochTimer
  from skorch.callbacks import PassthroughScoring
  from skorch.callbacks import PrintLog
  from skorch.utils import check_is_fitted
  from skorch.utils import get_dim
  from skorch.utils import is_dataset
  from skorch.utils import to_numpy
  from skorch.utils import to_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_exact_gp_regr_doc(doc) and returns: doc