code: def __init__(
      self,
      n_vocab,
      n_text_ctx,
      n_text_state,
      n_text_head,
      n_text_layer,
      **_,
  ):
      self.max_tokens_to_sample = n_text_ctx // 2
      self.max_self_attn_cache_len = (
          self.max_tokens_to_sample * 2 + 5
      )  # roughly prompt + start toks + max_tokens_to_sample
  
      self.token_embedding = nn.Embedding(n_vocab, n_text_state)
      self.positional_embedding = Tensor.empty(n_text_ctx, n_text_state)
      self.blocks = [
          ResidualAttentionBlock(
              n_text_state,
              n_text_head,
              is_decoder_block=True,
              max_self_attn_cache_len=self.max_self_attn_cache_len,
          )
          for _ in range(n_text_layer)
      ]
      self.ln = nn.LayerNorm(n_text_state)
      self.mask = (
          Tensor.full((n_text_ctx, n_text_ctx), -np.inf)
          .triu(1)
          .realize()
      )
      self.blocks_start_tok = [
          TinyJit(block.__call__) for block in self.blocks
      ]
      self.blocks_after_start_tok = [
          TinyJit(block.__call__) for block in self.blocks
      ]
      self.start_output_tok = TinyJit(self.output_tok)
      self.after_start_output_tok = TinyJit(self.output_tok)
  
imports: import sys
  import pathlib
  import base64
  import multiprocessing
  import numpy
  from typing import Optional, Union, Literal, List
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import torch_load, load_state_dict
  from tinygrad.helpers import getenv, DEBUG, CI, fetch
  import tinygrad.nn
  from tinygrad.shape.symbolic import Variable
  from tinygrad.tensor import Tensor
  import itertools
  import librosa
label_desc: Use image node_id 1 to create a new TextDecoder object  with example: obj = TextDecoder(n_vocab, n_text_ctx, n_text_state, n_text_head, n_text_layer)