class_docstr: Classification of data's device and whether it is a batch.
  
  Based on data type determines if data should be treated as a batch and with which device.
  If the type can be recognized as a batch without being falsely categorized as such, it is.
  This includes lists of supported tensor-like objects e.g. numpy arrays (the only list not
  treated as a batch is a list of objects of primitive types), :class:`DataNodeDebug` and
  TensorLists.
  
  Args:
      data: Data to be classified.
      type_name (str): Representation of argument type (input or keyword).
      arg_constant_len (int): Only applicable for argument inputs that are of array type
          (e.g. numpy array). If -1 does not modify the data. For positive value works like
          `:func:types.Constant`, repeats the data `arg_constant_len` times.
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code: def __init__(self, data, type_name, arg_constant_len=-1):
      from nvidia.dali._debug_mode import DataNodeDebug
  
      is_batch, device, extracted = self._classify_data(
          data, type_name, arg_constant_len
      )
      self.is_batch = is_batch
      self.device = device
      self.data = extracted
      self.was_data_node = isinstance(data, DataNodeDebug)
      self.original = data
  
imports: import sys
  from nvidia.dali import backend
  from nvidia.dali import internal
  from nvidia.dali import ops
  from nvidia.dali import tensors
  from nvidia.dali import types
  from nvidia.dali.external_source import _prep_data_for_feed_input
label_desc: Use image node_id 1 to create a new _Classification object  with example: obj = _Classification(data, type_name, arg_constant_len)