code: def __init__(self, vocab_size, hidden_size, layers, dropout):
      self.hidden_size = hidden_size
  
      self.emb = Embedding(vocab_size - 1, hidden_size)
      self.rnn = LSTM(hidden_size, hidden_size, layers, dropout)
  
imports: from tinygrad.tensor import Tensor
  from tinygrad.jit import TinyJit
  from tinygrad.nn import Linear, Embedding
  from tinygrad.helpers import fetch
  import numpy
  from pathlib import Path
label_desc: Use image node_id 1 to create a new Prediction object  with example: obj = Prediction(vocab_size, hidden_size, layers, dropout)