code: def validate_train_inputs(
      model_name,
      learn_rate,
      batch_size,
      gradient_step,
      data_root,
      template_file,
      template_filename,
      steps,
      save_model_every,
      create_image_every,
      log_directory,
      name="embedding",
  ):
      assert model_name, f"{name} not selected"
      assert learn_rate, "Learning rate is empty or 0"
      assert isinstance(batch_size, int), "Batch size must be integer"
      assert batch_size > 0, "Batch size must be positive"
      assert isinstance(
          gradient_step, int
      ), "Gradient accumulation step must be integer"
      assert (
          gradient_step > 0
      ), "Gradient accumulation step must be positive"
      assert data_root, "Dataset directory is empty"
      assert os.path.isdir(data_root), "Dataset directory doesn't exist"
      assert os.listdir(data_root), "Dataset directory is empty"
      assert template_filename, "Prompt template file not selected"
      assert (
          template_file
      ), f"Prompt template file {template_filename} not found"
      assert os.path.isfile(
          template_file.path
      ), f"Prompt template file {template_filename} doesn't exist"
      assert steps, "Max steps is empty or 0"
      assert isinstance(steps, int), "Max steps must be integer"
      assert steps > 0, "Max steps must be positive"
      assert isinstance(
          save_model_every, int
      ), "Save {name} must be integer"
      assert save_model_every >= 0, "Save {name} must be positive or 0"
      assert isinstance(
          create_image_every, int
      ), "Create image must be integer"
      assert (
          create_image_every >= 0
      ), "Create image must be positive or 0"
      if save_model_every or create_image_every:
          assert log_directory, "Log directory is empty"
  
imports: import os
  from collections import namedtuple
  from contextlib import closing
  import torch
  import tqdm
  import html
  import datetime
  import csv
  import safetensors.torch
  import numpy
  from PIL import Image, PngImagePlugin
  from torch.utils.tensorboard import SummaryWriter
  from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
  import modules.textual_inversion.dataset
  from modules.textual_inversion.learn_schedule import LearnRateScheduler
  from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
  from modules.textual_inversion.logging import save_settings_to_file
label_desc: Use image node_id 9 for calling a global function with example usage: validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name) without return types