chunk_content:           trainable_params += param.numel()
      print(
          f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}"
      )
  Choose a base model checkpoint. For this example, we use the SegFormer B0 variant.
  In addition to the checkpoint, pass the label2id and id2label dictionaries to let the AutoModelForSemanticSegmentation class know that we’re
  interested in a custom base mode