class_docstr: Abstract base class that stores data as well as any extra metadata.
  
  This allows for subclassing `torch.Tensor` and `np.ndarray` through multiple inheritance.
  
  Metadata is stored in the form of a dictionary.
  
  Behavior should be the same as extended class (e.g., `torch.Tensor` or `np.ndarray`)
  aside from the extended meta functionality.
  
  Copying of information:
  
      * For `c = a + b`, then auxiliary data (e.g., metadata) will be copied from the
        first instance of `MetaObj` if `a.is_batch` is False
        (For batched data, the metadata will be shallow copied for efficiency purposes).
code: def __init__(self) -> None:
      self._meta: dict = MetaObj.get_default_meta()
      self._applied_operations: list = (
          MetaObj.get_default_applied_operations()
      )
      self._pending_operations: list = (
          MetaObj.get_default_applied_operations()
      )  # the same default as applied_ops
      self._is_batch: bool = False
  
imports: from __future__ import annotations
  import itertools
  import pprint
  from copy import deepcopy
  from typing import Any, Iterable
  import numpy
  import torch
  from monai.utils import TraceKeys, first, is_immutable
label_desc: Use image node_id 1 to create a new MetaObj object  with example: obj = MetaObj()