code: def generate_array(
      initializer: types.AbstractInitializer,
      shape: types.ShapeSpec,
      xp: types.Xp,
      dtype: tp.Optional[types.DTypeSpec] = None,
      device: tp.Optional[types.DeviceSpec] = None,
  ) -> types.NdArray:
      """Return initialized array.
  
      The algorithms used to make the new values depend on the
      concrete derived classes. If the initializer has the ``dtype`` attribute,
      it is used to construct the array. Otherwise, ``chainer.config.dtype`` is
      used instead. See :ref:`configuration` for the dtype config.
  
      Args:
          initializer: A callable object that takes :ref:`ndarray` and edits its
              value.
          shape (int or tuple of int): Shape of the initialized array.
          xp (module): :mod:`cupy`, :mod:`numpy`, or :mod:`chainerx`.
          dtype: Dtype specifier. If omitted, ``initializer.dtype`` is used.
          device: Target device specifier. If omitted, the current device is
               used for :mod:`cupy`, and the default device is used for
               :mod:`chainerx`.
  
      Returns:
          :ref:`ndarray`: An initialized array.
  
      """
      dtype_attr = getattr(initializer, "dtype", None)
      if (
          dtype is not None
          and dtype_attr is not None
          and numpy.dtype(dtype) != numpy.dtype(dtype_attr)
      ):
          raise ValueError(
              "dtype mismatch: {} != {}".format(dtype, dtype_attr)
          )
      if dtype is None:
          dtype = dtype_attr
      dtype = chainer.get_dtype(dtype)
  
      if device is None:
          backend_device = backend._guess_device_from_array_module(xp)
      else:
          backend_device = chainer.get_device(device)
          if xp != backend_device.xp:
              raise ValueError(
                  "xp and device arguments are inconsistent."
              )
      with chainer.using_device(backend_device):
          array = xp.empty(shape, dtype=dtype)
          initializer(array)
      return array
  
imports: import typing
  import numpy
  import chainer
  from chainer import backend
  from chainer.backends import _chainerx
  from chainer.initializers.constant import Constant
  from chainer.initializers.constant import Identity
  from chainer.initializers.constant import NaN
  from chainer.initializers.constant import One
  from chainer.initializers.constant import Zero
  from chainer.initializers.normal import GlorotNormal
  from chainer.initializers.normal import HeNormal
  from chainer.initializers.normal import LeCunNormal
  from chainer.initializers.normal import Normal
  from chainer.initializers.orthogonal import Orthogonal
  from chainer.initializers.sampling import DownsamplingConvFilter
  from chainer.initializers.sampling import UpsamplingDeconvFilter
  from chainer.initializers.uniform import GlorotUniform
  from chainer.initializers.uniform import HeUniform
  from chainer.initializers.uniform import LeCunUniform
  from chainer.initializers.uniform import Uniform
  from chainer import types
label_desc: Use image node_id 1 for calling a global function with example usage: generate_array(initializer, shape, xp, dtype, device) and returns: array