class_docstr: CLI describes a command line interface for interacting with igel, there
  are several different functions that can be performed.
class_docstr_tok: ["CLI","describes","a","command","line","interface","for","interacting","with","igel",",","there","are","several","different","functions","that","can","be","performed","."]
docstr: initialize a dummy/default yaml file as a starting point. The user can provide args directly in the terminal
  usage:
      igel init <args>
  
  if not args are provided, the user will be prompted to enter basic information.
docstr_tok: ["initialize","a","dummy\/default","yaml","file","as","a","starting","point",".","The","user","can","provide","args","directly","in","the","terminal","usage",":","igel","init","<","args",">","if","not","args","are","provided",",","the","user","will","be","prompted","to","enter","basic","information","."]
code: def init(self, *args, **kwargs):
      """
      initialize a dummy/default yaml file as a starting point. The user can provide args directly in the terminal
      usage:
          igel init <args>
  
      if not args are provided, the user will be prompted to enter basic information.
      """
      d = dict(self.dict_args)
      d.pop("cmd")
      if not d:
          print(
              f""
              f"{'*' * 10} You entered interactive mode! {'*' * 10} \n"
              f"This is happening because you didn't enter all mandatory arguments in order to use the cli\n"
              f"Therefore, you will need to provide few information before proceeding.\n"
          )
          model_type = (
              input(
                  f"enter type of the problem you want to solve: [regression]       "
              )
              or "regression"
          )
          d["model_type"] = model_type
          model_name = (
              input(
                  f"enter algorithm you want to use: [NeuralNetwork]        "
              )
              or "NeuralNetwork"
          )
          d["model_name"] = model_name
          target = input(
              f"enter the target you want to predict  "
              "(this is usually a column name in your csv dataset):        "
          )
          d["target"] = target
  
      Igel.create_init_mock_file(**d)
  
imports: import argparse
  import logging
  import os
  import subprocess
  import sys
  from pathlib import Path
  import igel
  import pandas
  from igel import Igel, metrics_dict, models_dict
  from igel.constants import Constants
  from igel.servers import fastapi_server
label_desc: Use image node_id 7 for calling the CLI obj's underlying member method code with example usage: obj.init() without return types