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).
docstr: String representation of class.
docstr_tok: ["String","representation","of","class","."]
code: def __repr__(self) -> str:
      """String representation of class."""
      out: str = "\nMetadata\n"
      if self.meta is not None:
          out += "".join(f"\t{k}: {v}\n" for k, v in self.meta.items())
      else:
          out += "None"
  
      out += "\nApplied operations\n"
      if self.applied_operations is not None:
          out += pprint.pformat(
              self.applied_operations, indent=2, compact=True, width=120
          )
      else:
          out += "None"
  
      out += f"\nIs batch?: {self.is_batch}"
  
      return out
  
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 7 for calling the MetaObj obj's underlying member method code with example usage: obj.__repr__() and returns: out