code: def generate(
      self,
      start_tokens,
      seq_len,
      eos_token=None,
      temperature=1.0,
      filter_thres=0.9,
      **kwargs,
  ):
      b, t, device = *start_tokens.shape, start_tokens.device
  
      out = start_tokens
  
      for _ in range(seq_len):
          logits = self.net(out, **kwargs)[:, -1, :]
  
          filtered_logits = top_k(logits, thres=filter_thres)
          probs = F.softmax(filtered_logits / temperature, dim=-1)
  
          sample = torch.multinomial(probs, 1)
  
          out = torch.cat((out, sample), dim=-1)
  
          if exists(eos_token):
              is_eos_token = out == eos_token
  
              if is_eos_token.any(dim=-1).all():
                  # mask out everything after the eos tokens
                  shifted_is_eos_tokens = F.pad(is_eos_tokens, (1, -1))
                  mask = (
                      shifted_is_eos_tokens.float().cumsum(dim=-1) >= 1
                  )
                  out = out.masked_fill(mask, self.pad_value)
                  break
  
      out = out[:, t:]
      return out
  
file_path: ColossalAI/examples/language/palm/palm_pytorch/autoregressive_wrapper.py
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import nn
label_desc: Use image node_id 2 for calling the AutoregressiveWrapper obj's underlying member method code with example usage: obj.generate(start_tokens, seq_len, eos_token, temperature, filter_thres) and returns: out