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: Set the applied operations.
docstr_tok: ["Set","the","applied","operations","."]
code: def applied_operations(self, t) -> None:
      """Set the applied operations."""
      if t == TraceKeys.NONE:
          # received no operations when decollating a batch
          self._applied_operations = (
              MetaObj.get_default_applied_operations()
          )
          return
      self._applied_operations = t
  
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 11 for calling the MetaObj obj's underlying member method code with example usage: obj.applied_operations(t) without return types