code: def may_share_memory(a, b, raise_other_type=True):
      a_ndarray = isinstance(a, np.ndarray)
      b_ndarray = isinstance(b, np.ndarray)
      if a_ndarray and b_ndarray:
          return TensorType.may_share_memory(a, b)
      a_gpua = _is_gpua(a)
      b_gpua = _is_gpua(b)
      if a_gpua and b_gpua:
          return gpuarray.pygpu.gpuarray.may_share_memory(a, b)
  
      a_sparse = _is_sparse(a)
      b_sparse = _is_sparse(b)
      if not (a_ndarray or a_sparse or a_gpua) or not (
          b_ndarray or b_sparse or b_gpua
      ):
          if raise_other_type:
              raise TypeError(
                  "may_share_memory support only ndarray"
                  " and scipy.sparse or GpuArray type"
              )
          return False
  
      if a_gpua or b_gpua:
          return False
      return SparseType.may_share_memory(a, b)
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  from theano.tensor.basic import TensorType
  from theano import gpuarray
label_desc: Use image node_id 1 for calling a global function with example usage: may_share_memory(a, b, raise_other_type) and returns: SparseType, TensorType, gpuarray, False, False