code: def test_tensor_gpu_squeeze():
      def check_squeeze(shape, dim, in_layout, expected_out_layout):
          arr = cp.random.rand(*shape)
          t = TensorGPU(arr, in_layout)
          is_squeezed = t.squeeze(dim)
          should_squeeze = len(expected_out_layout) < len(in_layout)
          arr_squeeze = arr.squeeze(dim)
          t_shape = tuple(t.shape())
          assert (
              t_shape == arr_squeeze.shape
          ), f"{t_shape} != {arr_squeeze.shape}"
          assert (
              t.layout() == expected_out_layout
          ), f"{t.layout()} != {expected_out_layout}"
          assert cp.allclose(arr_squeeze, cp.asanyarray(t))
          assert (
              is_squeezed == should_squeeze
          ), f"{is_squeezed} != {should_squeeze}"
  
      for dim, shape, in_layout, expected_out_layout in [
          (None, (3, 5, 6), "ABC", "ABC"),
          (None, (3, 1, 6), "ABC", "AC"),
          (1, (3, 1, 6), "ABC", "AC"),
          (-2, (3, 1, 6), "ABC", "AC"),
          (None, (1, 1, 6), "ABC", "C"),
          (1, (1, 1, 6), "ABC", "AC"),
          (None, (1, 1, 1), "ABC", ""),
          (None, (1, 5, 1), "ABC", "B"),
          (-1, (1, 5, 1), "ABC", "AB"),
          (0, (1, 5, 1), "ABC", "BC"),
          (None, (3, 5, 1), "ABC", "AB"),
      ]:
          yield check_squeeze, shape, dim, in_layout, expected_out_layout
  
imports: from nvidia.dali.backend_impl import TensorGPU, TensorListGPU
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.tensors
  import nvidia.dali.types
  import numpy
  from nose_utils import assert_raises, raises
  import cupy
  from test_utils import py_buffer_from_address
label_desc: Use image node_id 27 for calling a global function with example usage: test_tensor_gpu_squeeze() without return types