code: def batched_sample(
      self, logits, temperature, top_k, top_p, min_p, typical, num=1
  ):
      if logits.shape[0] == 1:
          return self.sample(
              logits, temperature, top_k, top_p, min_p, typical, num
          )
  
      samples = []
      scores = []
      for i in range(logits.shape[0]):
          t, s = self.sample(
              logits[i, :, :], temperature, top_k, top_p, min_p, typical
          )
          samples.append(t)
          scores.append(s)
  
      return torch.cat(samples, dim=0), torch.cat(scores, dim=0)
  
imports: import cuda_ext
  from model import ExLlama, ExLlamaCache
  from lora import ExLlamaLora
  import torch
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the ExLlamaGenerator obj's underlying member method code with example usage: obj.batched_sample(logits, temperature, top_k, top_p, min_p, typical, num) and returns: torch,  torch, self