code: def is_np_shape():
      """Checks whether the NumPy shape semantics is currently turned on.
      In NumPy shape semantics, `()` represents the shape of scalar tensors,
      and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent
      the shapes of zero-size tensors. This is turned off by default for keeping
      backward compatibility.
  
      In the NumPy shape semantics, `-1` indicates an unknown size. For example,
      `(-1, 2, 2)` means that the size of the first dimension is unknown. Its size
      may be inferred during shape inference.
  
      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 within this semantics.
  
      Returns
      -------
          A bool value indicating whether the NumPy shape semantics is currently on.
  
      Example
      -------
      >>> import mxnet as mx
      >>> prev_state = mx.set_np_shape(True)
      >>> print(prev_state)
      False
      >>> print(mx.is_np_shape())
      True
      """
      curr = ctypes.c_bool()
      check_call(_LIB.MXIsNumpyShape(ctypes.byref(curr)))
      return curr.value
  
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 4 for calling a global function with example usage: is_np_shape() and returns: curr