code: def generate(
      self,
      image,
      sample=False,
      num_beams=3,
      max_length=30,
      min_length=10,
      top_p=0.9,
      repetition_penalty=1.0,
  ):
      image_embeds = self.visual_encoder(image)
  
      if not sample:
          image_embeds = image_embeds.repeat_interleave(
              num_beams, dim=0
          )
  
      image_atts = torch.ones(
          image_embeds.size()[:-1], dtype=torch.long
      ).to(image.device)
      model_kwargs = {
          "encoder_hidden_states": image_embeds,
          "encoder_attention_mask": image_atts,
      }
  
      prompt = [self.prompt] * image.size(0)
      input_ids = self.tokenizer(
          prompt, return_tensors="pt"
      ).input_ids.to(image.device)
      input_ids[:, 0] = self.tokenizer.bos_token_id
      input_ids = input_ids[:, :-1]
  
      if sample:
          # nucleus sampling
          outputs = self.text_decoder.generate(
              input_ids=input_ids,
              max_length=max_length,
              min_length=min_length,
              do_sample=True,
              top_p=top_p,
              num_return_sequences=1,
              eos_token_id=self.tokenizer.sep_token_id,
              pad_token_id=self.tokenizer.pad_token_id,
              repetition_penalty=1.1,
              **model_kwargs,
          )
      else:
          # beam search
          outputs = self.text_decoder.generate(
              input_ids=input_ids,
              max_length=max_length,
              min_length=min_length,
              num_beams=num_beams,
              eos_token_id=self.tokenizer.sep_token_id,
              pad_token_id=self.tokenizer.pad_token_id,
              repetition_penalty=repetition_penalty,
              **model_kwargs,
          )
  
      captions = []
      for output in outputs:
          caption = self.tokenizer.decode(
              output, skip_special_tokens=True
          )
          captions.append(caption[len(self.prompt) :])
      return captions
  
imports: import warnings
  from models.vit import VisionTransformer, interpolate_pos_embed
  from models.med import BertConfig, BertModel, BertLMHeadModel
  from transformers import BertTokenizer
  import torch
  from torch import nn
  import torch.nn.functional
  import os
  from urllib.parse import urlparse
  from timm.models.hub import download_cached_file
label_desc: Use image node_id 3 for calling the BLIP_Decoder obj's underlying member method code with example usage: obj.generate(image, sample, num_beams, max_length, min_length, top_p, repetition_penalty) and returns: captions