code: def render_model(
      params,
      model_name,
      framework,
      env,
      problem_type,
      target_metric,
      target_field,
      train_folder,
      fields,
      split,
      num_epochs,
      gpu,
      tpu_address,
      metrics_path=resource_filename(__name__, "metrics.yml"),
  ):
      """Renders and saves the files (model.py, pipeline.py, requirements.txt) for the given hyperparameters."""
  
      files = ["model.py", "pipeline.py", "requirements.txt"]
  
      type_map = {
          "numeric": "float64",
          "categorical": "str",
          "datetime": "str",
          "text": "str",
      }
  
      load_fields = {field[1]: type_map[field[2]] for field in fields}
      text_fields = [field for field in fields if field[2] == "text"]
      nontarget_fields = [
          field for field in fields if field[1] != target_field
      ]
      target_field, target_field_raw = [
          (field[0], field[1])
          for field in fields
          if field[1] == target_field
      ][0]
      has_text_input = "text" in [field[2] for field in fields]
      text_framework = (
          "tensorflow" if framework == "tensorflow" else "sklearn"
      )
  
      with open(metrics_path) as f:
          metrics = yaml.safe_load(f)[problem_type]
  
      for file in files:
          script = env.get_template(
              "scripts/" + file.replace(".py", "")
          ).render(
              params=params,
              model_name=model_name,
              framework=framework,
              problem_type=problem_type,
              target_metric=target_metric,
              target_field=target_field,
              fields=fields,
              split=split,
              num_epochs=num_epochs,
              load_fields=load_fields,
              text_fields=text_fields,
              nontarget_fields=nontarget_fields,
              target_field_raw=target_field_raw,
              has_text_input=has_text_input,
              metrics=metrics,
              text_framework=text_framework,
              gpu=gpu,
              tpu_address=tpu_address,
          )
  
          script = fix_code(script)
  
          with open(
              train_folder + "/" + file, "w", encoding="utf8"
          ) as outfile:
              outfile.write(script)
  
imports: import re
  import pandas
  import random
  import yaml
  import os
  import shutil
  from time import time
  from pkg_resources import resource_filename
  from tqdm import tqdm, tqdm_notebook
  from tqdm._utils import _term_move_up
  from subprocess import Popen, PIPE, DEVNULL, CalledProcessError
  from autopep8 import fix_code
  from collections import OrderedDict
label_desc: Use image node_id 5 for calling a global function with example usage: render_model(params, model_name, framework, env, problem_type, target_metric, target_field, train_folder, fields, split, num_epochs, gpu, tpu_address, metrics_path) without return types