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 __init__(
      self,
      model_path=None,
      name: str = None,
      description: str = None,
      details: Dict = None,
      tasks: List[str] = None,
      device=None,
  ):
      tasks = tasks or ["text-generation", "summarisation"]
      HFSeq2SeqTGModel.__init__(
          self,
          model_path=model_path,
          name=name,
          description=description,
          tasks=tasks,
          details=details,
          device=device,
      )
  
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 1 to create a new T5 object from inherited base classes: HFSeq2SeqTGModel with example: obj = T5(model_path, name, description, details, tasks, device)