code: def _concatenate(mini_batch, key):
      if isinstance(mini_batch[key], torch.Tensor):
          return torch.concat([mini_batch[key]])
      elif isinstance(mini_batch[key], tf.Tensor):
          return tf.concat([mini_batch[key]], 0)
      else:
          return np.concatenate([mini_batch[key]])
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/conversions/huggingface.py
imports: from typing import List, Dict, Sequence, Optional
  import numpy
  from nebullvm.core.models import Device
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.optional_modules.huggingface import PreTrainedTokenizer, PreTrainedModel
  from nebullvm.tools.huggingface import get_output_structure_from_dict, get_output_structure_from_text, PyTorchTransformerWrapper, TensorFlowTransformerWrapper
  from nebullvm.tools.utils import is_dict_type
label_desc: Use image node_id 4 for calling the _HFDictDataset obj's underlying member method code with example usage: obj._concatenate(mini_batch, key) and returns: torch, tf, np