class_docstr: Igel is the base model to use the fit, evaluate and predict functions of the sklearn library
class_docstr_tok: ["Igel","is","the","base","model","to","use","the","fit",",","evaluate","and","predict","functions","of","the","sklearn","library"]
code: def create_init_mock_file(
      model_type=None, model_name=None, target=None, *args, **kwargs
  ):
      path = configs.get("init_file_path", None)
      if not path:
          raise Exception(
              "You need to provide a path for the init file"
          )
  
      dataset_props = Igel.default_dataset_props
      model_props = Igel.default_model_props
      if model_type:
          logger.info(f"user selected model type = {model_type}")
          model_props["type"] = model_type
      if model_name:
          logger.info(f"user selected algorithm = {model_name}")
          model_props["algorithm"] = model_name
  
      logger.info(f"initalizing a default igel.yaml in {path}")
      default_data = {
          "dataset": dataset_props,
          "model": model_props,
          "target": ["provide your target(s) here"]
          if not target
          else [tg for tg in target.split()],
      }
      created = create_yaml(default_data, path)
      if created:
          logger.info(
              f"a default igel.yaml is created for you in {path}. "
              f"you just need to overwrite the values to meet your expectations"
          )
      else:
          logger.warning(
              f"something went wrong while initializing a default file"
          )
  
imports: import json
  import logging
  import os
  import warnings
  import joblib
  import numpy
  import pandas
  from sklearn.model_selection import cross_validate, train_test_split
  from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
label_desc: Use image node_id 16 for calling the Igel obj's underlying member method code with example usage: obj.create_init_mock_file(model_type, model_name, target) without return types