code: def test_crop_inputs(self):
      from lasagne.layers.merge import autocrop
      from numpy.testing import assert_array_equal
  
      crop_0 = None
      crop_1 = [None, "lower", "center", "upper"]
      crop_l = ["lower", "lower", "lower", "lower"]
      crop_c = ["center", "center", "center", "center"]
      crop_u = ["upper", "upper", "upper", "upper"]
      crop_x = ["lower", "lower"]
      crop_bad = ["lower", "lower", "bad", "worse"]
  
      x0 = numpy.random.random((2, 3, 5, 7))
      x1 = numpy.random.random((1, 2, 3, 4))
      x2 = numpy.random.random((6, 3, 4, 2))
  
      def crop_test(cropping, inputs, expected):
          inputs = [theano.shared(x) for x in inputs]
          outs = autocrop(inputs, cropping)
          outs = [o.eval() for o in outs]
          assert len(outs) == len(expected)
          for o, e in zip(outs, expected):
              assert_array_equal(o, e)
  
      crop_test(crop_0, [x0, x1], [x0, x1])
      crop_test(crop_1, [x0, x1], [x0[:, :2, 1:4, 3:], x1[:, :, :, :]])
      crop_test(crop_l, [x0, x1], [x0[:1, :2, :3, :4], x1[:, :, :, :]])
      crop_test(
          crop_c, [x0, x1], [x0[:1, :2, 1:4, 1:5], x1[:, :, :, :]]
      )
      crop_test(crop_u, [x0, x1], [x0[1:, 1:, 2:, 3:], x1[:, :, :, :]])
  
      crop_test(crop_0, [x0, x2], [x0, x2])
      crop_test(crop_1, [x0, x2], [x0[:, :, :4, 5:], x2[:, :, :, :]])
      crop_test(crop_l, [x0, x2], [x0[:, :, :4, :2], x2[:2, :, :, :]])
      crop_test(crop_c, [x0, x2], [x0[:, :, :4, 2:4], x2[2:4, :, :, :]])
      crop_test(crop_u, [x0, x2], [x0[:, :, 1:, 5:], x2[4:, :, :, :]])
  
      crop_test(crop_0, [x0, x1, x2], [x0, x1, x2])
      crop_test(
          crop_1,
          [x0, x1, x2],
          [x0[:, :2, 1:4, 5:], x1[:, :, :, 2:], x2[:, :2, :3, :]],
      )
      crop_test(
          crop_l,
          [x0, x1, x2],
          [x0[:1, :2, :3, :2], x1[:, :, :, :2], x2[:1, :2, :3, :]],
      )
      crop_test(
          crop_c,
          [x0, x1, x2],
          [x0[:1, :2, 1:4, 2:4], x1[:, :, :, 1:3], x2[2:3, :2, :3, :]],
      )
      crop_test(
          crop_u,
          [x0, x1, x2],
          [x0[1:, 1:, 2:, 5:], x1[:, :, :, 2:], x2[5:, 1:, 1:, :]],
      )
  
      crop_test(
          crop_x,
          [x0, x1, x2],
          [x0[:1, :2, :, :], x1[:1, :2, :, :], x2[:1, :2, :, :]],
      )
  
      # test that num outputs is correct when the number of inputs is
      # larger than ndim of the inputs.
      crop_test(
          crop_x,
          [x0, x1, x2, x0, x1, x2],
          [
              x0[:1, :2, :, :],
              x1[:1, :2, :, :],
              x2[:1, :2, :, :],
              x0[:1, :2, :, :],
              x1[:1, :2, :, :],
              x2[:1, :2, :, :],
          ],
      )
  
      with pytest.raises(ValueError):
          crop_test(
              crop_bad,
              [x0, x1, x2],
              [x0[:1, :2, :, :], x1[:1, :2, :, :], x2[:1, :2, :, :]],
          )
  
      # Inconsistent dimensionality
      with pytest.raises(ValueError):
          crop_test(
              crop_bad,
              [x0[:, :, :, 0], x1, x2[:, :, :, :, None]],
              [x0[:1, :2, :, :], x1[:1, :2, :, :], x2[:1, :2, :, :]],
          )
  
imports: from mock import Mock
  import numpy
  import pytest
  import theano
label_desc: Use image node_id 2 for calling the TestAutocrop obj's underlying member method code with example usage: obj.test_crop_inputs() without return types