code: def plot_batch(cls, batch, cfg) -> PlotData:
      tokenizer = get_tokenizer(cfg)
      df = create_batch_prediction_df(
          batch, tokenizer, ids_for_tokenized_text="prompt_input_ids"
      )
      path = os.path.join(cfg.output_directory, "batch_viz.parquet")
      df.to_parquet(path)
      return PlotData(path, encoding="df")
  
imports: import os
  from llm_studio.src.datasets.text_utils import get_tokenizer
  from llm_studio.src.plots.text_causal_language_modeling_plots import Plots
  from llm_studio.src.plots.text_causal_language_modeling_plots import create_batch_prediction_df
  from llm_studio.src.utils.plot_utils import PlotData
label_desc: Use image node_id 1 for calling the Plots obj's underlying member method code with example usage: obj.plot_batch(cls, batch, cfg) and returns: PlotData