code: def crop_to_max_size(self, t, target_size, dim=0):
      size = t.size(dim)
      diff = size - target_size
      if diff <= 0:
          return t
  
      start = np.random.randint(0, diff + 1)
      end = size - diff + start
  
      slices = []
      for d in range(dim):
          slices.append(slice(None))
      slices.append(slice(start, end))
  
      return t[slices]
  
imports: import logging
  import os
  import sys
  import time
  import io
  import numpy
  import torch
  import torch.nn.functional
  from ..None import FairseqDataset
  from ..data_utils import compute_block_mask_1d, get_buckets, get_bucketed_sizes
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip, is_sf_audio_data
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
label_desc: Use image node_id 5 for calling the RawAudioDataset obj's underlying member method code with example usage: obj.crop_to_max_size(t, target_size, dim) and returns: t, t