code: def sample(
      model, x, steps, temperature=1.0, sample=False, top_k=None
  ):
      """
      take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
      the sequence, feeding the predictions back into the model each time. Clearly the sampling
      has quadratic complexity unlike an RNN that is only linear, and has a finite context window
      of block_size, unlike an RNN that has an infinite context window.
      """
      block_size = model.get_block_size()
      model.eval()
  
      # CREDITS: https://github.com/karpathy/minGPT/blob/master/mingpt/utils.py
      def top_k_logits(logits, k):
          v, _ = torch.topk(logits, k)
          out = logits.clone()
          out[out < v[:, [-1]]] = -float("Inf")
          return out
  
      for _ in range(steps):
          x_cond = (
              x if x.size(1) <= block_size else x[:, -block_size:]
          )  # crop context if needed
          logits = model(x_cond)
  
          # pluck the logits at the final step and scale by temperature
          logits = logits[:, -1, :] / temperature
  
          # optionally crop probabilities to only the top k options
          if top_k is not None:
              logits = top_k_logits(logits, top_k)
  
          # apply softmax to convert to probabilities
          probs = F.softmax(logits, dim=-1)
  
          # sample from the distribution or take the most likely
          if sample:
              ix = torch.multinomial(probs, num_samples=1)
          else:
              _, ix = torch.topk(probs, k=1, dim=-1)
  
          # append to the sequence and continue
          x = torch.cat((x, ix), dim=1)
  
      return x[0]
  
imports: import math
  import os
  import pytorch_lightning
  import torch
  import torch.nn
  from pytorch_lightning import Trainer, seed_everything
  from pytorch_lightning.utilities import rank_zero_info
  from torch.nn import functional
  from torch.utils.data import DataLoader, Dataset, RandomSampler
  from xformers.factory.model_factory import xFormer, xFormerConfig
label_desc: Use image node_id 1 for calling a global function with example usage: sample(model, x, steps, temperature, sample, top_k) and returns: x, out