code: def cupy_tensorflow_allocator(size_in_bytes: int):
      """Function that can be passed into cupy.cuda.set_allocator, to have cupy
      allocate memory via TensorFlow. This is important when using the two libraries
      together, as otherwise OOM errors can occur when there's available memory
      sitting in the other library's pool.
      """
      size_in_bytes = max(1024, size_in_bytes)
      tensor = tensorflow.zeros((size_in_bytes // 4,), dtype=tensorflow.dtypes.float32)  # type: ignore
      # We convert to cupy via dlpack, so that we can get a memory pointer.
      cupy_array = cast(ArrayXd, tensorflow2xp(tensor))
      address = int(cupy_array.data)
      # cupy has a neat class to help us here. Otherwise it will try to free.
      memory = cupy.cuda.memory.UnownedMemory(
          address, size_in_bytes, cupy_array
      )
      # Now return a new memory pointer.
      return cupy.cuda.memory.MemoryPointer(memory, 0)
  
imports: from typing import cast
  from ..compat import cupy, tensorflow, torch
  from ..types import ArrayXd
  from ..util import get_torch_default_device, tensorflow2xp
label_desc: Use image node_id 1 for calling a global function with example usage: cupy_tensorflow_allocator(size_in_bytes) and returns: cupy