code: def _stack_arrs(arrs, use_shared_mem, dtype):
      if isinstance(arrs[0], mx.nd.NDArray):
          dtype = dtype or arrs[0].dtype
          if use_shared_mem:
              if is_np_array():
                  out = mx.np.empty(
                      (len(arrs),) + arrs[0].shape,
                      dtype=dtype,
                      ctx=mx.Context("cpu_shared", 0),
                  )
                  return mx.np.stack(arrs, out=out)
              else:
                  out = mx.nd.empty(
                      (len(arrs),) + arrs[0].shape,
                      dtype=dtype,
                      ctx=mx.Context("cpu_shared", 0),
                  )
                  return mx.nd.stack(*arrs, out=out)
          else:
              if is_np_array():
                  return mx.np.stack(arrs)
              else:
                  return mx.nd.stack(*arrs)
      else:
          out = np.asarray(arrs)
          dtype = dtype or out.dtype
          if use_shared_mem:
              if is_np_array():
                  return mx.np.array(
                      out, ctx=mx.Context("cpu_shared", 0), dtype=dtype
                  )
              else:
                  return mx.nd.array(
                      out, ctx=mx.Context("cpu_shared", 0), dtype=dtype
                  )
          else:
              if is_np_array():
                  return mx.np.array(out, dtype=dtype)
              else:
                  return mx.nd.array(out, dtype=dtype)
  
imports: import warnings
  import math
  from typing import Dict, Callable, NamedTuple, List, Tuple, AnyStr, Union
  import numpy
  import mxnet
  from mxnet.util import is_np_array
label_desc: Use image node_id 2 for calling a global function with example usage: _stack_arrs(arrs, use_shared_mem, dtype) and returns: mx, mx, mx, mx, mx, mx, mx, mx