code: def generate_random_data_sample(T, B=1, D=80):
      """Generate random data sample given the T, B, D values"""
      net_input = {
          "src_tokens": torch.tensor(
              random_number_generator.randn(B, T, D)
          ).float(),
          "src_lengths": torch.tensor([T]),
      }
      return {"net_input": net_input}
  
imports: from fairseq import tasks
  import numpy
  import logging
  import random
  from fairseq import options
  import torch
  import os
  import soundfile
  from fairseq.data.audio.audio_utils import get_waveform, parse_path
label_desc: Use image node_id 1 for calling a global function with example usage: generate_random_data_sample(T, B, D) and returns: dict