class_docstr: Initialises a T5 model that has been finetuned on qa, summarisation and emotion detection.
  
  Attributes:
      model_path: path to an appropriate T5 model on huggingface (kiri-ai/t5-base-qa-summary-emotion)
      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.
class_docstr_tok: ["Initialises","a","T5","model","that","has","been","finetuned","on","qa",",","summarisation","and","emotion","detection",".","Attributes",":","model_path",":","path","to","an","appropriate","T5","model","on","huggingface","(","kiri-ai\/t5-base-qa-summary-emotion",")","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 encode_input(self, inp, max_length):
      tokens = self.tokenizer(
          inp,
          truncation=True,
          max_length=max_length,
          padding="max_length",
          return_tensors="pt",
      )
      return {
          "input_ids": tokens.input_ids[0],
          "attention_mask": tokens.attention_mask[0],
      }
  
file_path: backprop/backprop/models/t5_qa_summary_emotion/model.py
imports: from typing import List, Tuple, Dict
  import torch
  from backprop.models import HFSeq2SeqTGModel
label_desc: Use image node_id 9 for calling the T5QASummaryEmotion obj's underlying member method code with example usage: obj.encode_input(inp, max_length) and returns: dict