class_docstr: Initialises a T5 model.
  
  Attributes:
      model_path: path to an appropriate T5 model on huggingface (t5-small)
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      device: Device for model. Defaults to "cuda" if available.
code: def summary(self, text, task_prefix, **gen_kwargs):
      if isinstance(text, list):
          text = [f"{task_prefix}: {t}" for t in text]
      else:
          text = f"{task_prefix}: {text}"
  
      # TODO: Do sample beam
      return self.generate(
          text, max_length=200, min_length=30, **gen_kwargs
      )
  
file_path: backprop/backprop/models/t5/model.py
imports: from typing import List, Tuple, Dict
  import torch
  from backprop.models import HFSeq2SeqTGModel
label_desc: Use image node_id 4 for calling the T5 obj's underlying member method code with example usage: obj.summary(text, task_prefix) and returns: self