code: def np_default_dtype(active=True):
      """Returns an activated/deactivated NumPy-default_dtype scope to be used in 'with' statement
      and captures code that needs the NumPy default dtype semantics. i.e. default dtype is float64.
  
      Please note that this is designed as an infrastructure for the incoming
      MXNet-NumPy operators. Legacy operators registered in the modules
      `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts
      in NumPy even within this scope.
  
      Parameters
      ----------
      active : bool
          Indicates whether to activate NumPy default dtype semantics.
  
      Returns
      -------
      _NumpyDefaultDtypeScope
          A scope object for wrapping the code w/ or w/o NumPy-default_dtype semantics.
  
      Example::
  
          with mx.np_default_dtype(active=True):
              # Default Dtype is 'float64', consistent with offical NumPy behavior.
              arr = mx.np.array([1, 2, 3])
              assert arr.dtype == 'float64'
  
          with mx.np_default_dtype(active=False):
              # Default Dtype is 'float32' in the legacy default dtype definition.
              arr = mx.np.array([1, 2, 3])
              assert arr.dtype == 'float32'
  
      """
      return _NumpyDefaultDtypeScope(active)
  
imports: import ctypes
  import functools
  import inspect
  import threading
  import tempfile
  import platform
  from contextlib import contextmanager
  from struct import calcsize
  from .base import _LIB, check_call, c_str, py_str, numeric_types, integer_types, long, _MAX_VALUE_64_BIT_UNSIGNED_, _MAX_VALUE_64_BIT_SIGNED_, _MAX_VALUE_FLOAT32_REPRESENT_
label_desc: Use image node_id 26 for calling a global function with example usage: np_default_dtype(active) and returns: _NumpyDefaultDtypeScope