code: def _expand_dims_transpose(arr, mode, mode_out):
      """Return a reshaped and transposed array.
  
      The input array ``arr`` having ``mode`` as its modes is reshaped and
      transposed so that modes of the output becomes ``mode_out``.
  
      Example
          >>> import cupy
          >>> a = cupy.zeros((10, 20))
          >>> mode_a = ('A', 'B')
          >>> mode_out = ('B', 'C', 'A')
          >>> out = cupy.linalg.einsum._expand_dims_transpose(a, mode_a,
          ...                                                 mode_out)
          >>> out.shape
          (20, 1, 10)
  
      Args:
          arr (cupy.ndarray):
          mode (tuple or list): The modes of input array.
          mode_out (tuple or list): The modes of output array.
  
      Returns:
          cupy.ndarray: The reshaped and transposed array.
  
      """
      mode = list(mode)
      shape = list(arr.shape)
      axes = []
      for i in mode_out:
          if i not in mode:
              mode.append(i)
              shape.append(1)
          axes.append(mode.index(i))
      return cupy.transpose(arr.reshape(shape), axes)
  
imports: import copy
  import itertools
  import operator
  import string
  import warnings
  import cupy
  from cupy._core import _accelerator
  from cupy import _util
  from cupy.linalg._einsum_opt import _greedy_path
  from cupy.linalg._einsum_opt import _optimal_path
  from cupy.linalg._einsum_cutn import _try_use_cutensornet
label_desc: Use image node_id 11 for calling a global function with example usage: _expand_dims_transpose(arr, mode, mode_out) and returns: cupy