code: def __init__(
      self,
      binary_file_path: str,
      batch_size: int = 2048,
      drop_last_batch: bool = True,  # the last batch may not contain enough data which breaks the size of KJT
  ) -> None:
      self.dataset = ParametricDataset(
          binary_file_path,
          batch_size,
          drop_last_batch,
      )
      self._num_ids_in_batch: int = CAT_FEATURE_COUNT * batch_size
      self.keys: List[str] = DEFAULT_CAT_NAMES
      self.lengths: torch.Tensor = torch.ones(
          (self._num_ids_in_batch,), dtype=torch.int32
      )
      self.offsets: torch.Tensor = torch.arange(
          0, self._num_ids_in_batch + 1, dtype=torch.int32
      )
      self.stride = batch_size
      self.length_per_key: List[int] = CAT_FEATURE_COUNT * [batch_size]
      self.offset_per_key: List[int] = [
          batch_size * i for i in range(CAT_FEATURE_COUNT + 1)
      ]
      self.index_per_key: Dict[str, int] = {
          key: i for (i, key) in enumerate(self.keys)
      }
  
imports: import concurrent
  import math
  import os
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  from torch.utils import data
  from torch.utils.data import Dataset
  from torch.utils.data.distributed import DistributedSampler
  from torchrec.datasets.criteo import CAT_FEATURE_COUNT, DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.datasets.utils import Batch
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 to create a new NvtBinaryDataloader object  with example: obj = NvtBinaryDataloader(binary_file_path, batch_size, drop_last_batch)