code: def rand_sample_repeat(x, max_len):
      if x.shape[0] < max_len:
          indices = torch.randint(
              0, x.shape[0], (max_len - x.shape[0],)
          )
          # pdb.set_trace()
          return torch.cat((x, x[indices]), dim=0)
      elif x.shape[0] == max_len:
          return x
      else:
          rand_idx = torch.randperm(x.shape[0])[:max_len]
          return x[rand_idx, :]
  
imports: from abc import ABC, abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  import math
  from .multimodal_encoder.builder import build_vision_tower
  import pdb
  from ferret.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
label_desc: Use image node_id 2 for calling a global function with example usage: rand_sample_repeat(x, max_len) and returns: torch, x, x