// META: title=test WebNN API subgraph with multiple operations
// META: global=window,worker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long

'use strict';

const subgraphTests = [
  {
    'name': 'conv2d default + relu',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'relu',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5323282480239868, 1.3573521375656128, 1.3641656637191772,
            1.071682333946228, 1.1259644031524658, 1.4713115692138672,
            1.078782320022583, 1.155018925666809, 1.656954288482666
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + relu / float16',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6123046875,    0.8857421875,      0.13671875,
            0.564453125,     0.896484375,       0.367919921875,
            0.68115234375,   0.047943115234375, 0.33349609375,
            0.1988525390625, 0.41162109375,     0.079345703125,
            0.42724609375,   0.53564453125,     0.59130859375,
            0.2841796875,    0.414794921875,    0.0269012451171875,
            0.362060546875,  0.99462890625,     0.07183837890625,
            0.1220703125,    0.84228515625,     0.453857421875,
            0.21533203125
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float16'}
        },
        'conv2dFilter': {
          'data': [
            0.38037109375, 0.52783203125, 0.219482421875, 0.366943359375,
            0.33984375, 0.419921875, 0.380615234375, 0.1944580078125,
            0.56884765625
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float16'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'relu',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5322265625, 1.357421875, 1.3642578125, 1.0712890625, 1.1259765625,
            1.4716796875, 1.0791015625, 1.1552734375, 1.6572265625
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float16'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + reshape / float16',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6123046875,    0.8857421875,      0.13671875,
            0.564453125,     0.896484375,       0.367919921875,
            0.68115234375,   0.047943115234375, 0.33349609375,
            0.1988525390625, 0.41162109375,     0.079345703125,
            0.42724609375,   0.53564453125,     0.59130859375,
            0.2841796875,    0.414794921875,    0.0269012451171875,
            0.362060546875,  0.99462890625,     0.07183837890625,
            0.1220703125,    0.84228515625,     0.453857421875,
            0.21533203125
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float16'}
        },
        'conv2dFilter': {
          'data': [
            0.38037109375, 0.52783203125, 0.219482421875, 0.366943359375,
            0.33984375, 0.419921875, 0.380615234375, 0.1944580078125,
            0.56884765625
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float16'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'reshape',
          'arguments': [{'input': 'conv2dOutput'}, {'newShape': [9]}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5322265625, 1.357421875, 1.3642578125, 1.0712890625, 1.1259765625,
            1.4716796875, 1.0791015625, 1.1552734375, 1.6572265625
          ],
          'descriptor': {shape: [9], dataType: 'float16'}
        }
      }
    }
  },
  {
    'name': 'reshape + conv2d default/ float16',
    'graph': {
      'inputs': {
        'reshapeInput': {
          'data': [
            0.38037109375, 0.52783203125, 0.219482421875, 0.366943359375,
            0.33984375, 0.419921875, 0.380615234375, 0.1944580078125,
            0.56884765625
          ],
          'descriptor': {shape: [9], dataType: 'float16'},
        },
        'conv2dInput': {
          'data': [
            0.6123046875,    0.8857421875,      0.13671875,
            0.564453125,     0.896484375,       0.367919921875,
            0.68115234375,   0.047943115234375, 0.33349609375,
            0.1988525390625, 0.41162109375,     0.079345703125,
            0.42724609375,   0.53564453125,     0.59130859375,
            0.2841796875,    0.414794921875,    0.0269012451171875,
            0.362060546875,  0.99462890625,     0.07183837890625,
            0.1220703125,    0.84228515625,     0.453857421875,
            0.21533203125
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float16'}
        },
      },
      'operators': [
        {
          'name': 'reshape',
          'arguments': [{'input': 'reshapeInput'}, {'newShape': [1, 1, 3, 3]}],
          'outputs': 'reshapeOutput'
        },
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'reshapeOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5322265625, 1.357421875, 1.3642578125, 1.0712890625, 1.1259765625,
            1.4716796875, 1.0791015625, 1.1552734375, 1.6572265625
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float16'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + sigmoid',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'sigmoid',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            0.8223466873168945, 0.7953290343284607, 0.7964358925819397,
            0.7449167370796204, 0.7550933957099915, 0.8132566809654236,
            0.7462635040283203, 0.7604264616966248, 0.83982872962951666
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + clamp',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'clamp',
          'arguments': [
            {'input': 'conv2dOutput'},
            {'options': {'minValue': 0, 'maxValue': 6}}
          ],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5323282480239868, 1.3573521375656128, 1.3641656637191772,
            1.071682333946228, 1.1259644031524658, 1.4713115692138672,
            1.078782320022583, 1.155018925666809, 1.656954288482666
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      }
    }
  },
  {
    'name': 'conv2d default + leakyRelu',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'leakyRelu',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5323282480239868, 1.3573521375656128, 1.3641656637191772,
            1.071682333946228, 1.1259644031524658, 1.4713115692138672,
            1.078782320022583, 1.155018925666809, 1.656954288482666
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + elu',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'elu',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.5323282480239868, 1.3573521375656128, 1.3641656637191772,
            1.071682333946228, 1.1259644031524658, 1.4713115692138672,
            1.078782320022583, 1.155018925666809, 1.656954288482666
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + prelu',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            -0.8073334693908691, -0.8839999437332153,  -0.7700487375259399,
            -0.5646049380302429, -0.39717939496040344, -0.10841356962919235,
            -0.5519214868545532, -0.3954172134399414,  -0.057589758187532425,
            -0.5144240856170654, -0.21321901679039001, -0.950609028339386,
            -0.8043696880340576, -0.8646378517150879,  -0.9607220888137817,
            -0.3264438509941101, -0.06884296983480453, -0.3203399181365967,
            -0.2692728042602539, -0.3430887758731842,  -0.8989502191543579,
            -0.9038569331169128, -0.6369403004646301,  -0.20070797204971313,
            -0.7870702147483826,
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        },
        'preluSlope': {
          'data': [
            2,
            3,
            4,
            -2,
            -4,
            -5,
            8,
            9,
            1,
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        },
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'prelu',
          'arguments': [{'input': 'conv2dOutput'}, {'slope': 'preluSlope'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            -4.119449138641357, -6.7131500244140625, -8.318120002746582,
            2.9565374851226807, 6.632988929748535, 8.277504920959473,
            -15.338706970214844, -16.247453689575195, -2.055551290512085
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + hardSwish',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
            0.3621256649494171,  0.9945681691169739,  0.07184549421072006,
            0.12204372137784958, 0.8422137498855591,  0.4537501037120819,
            0.21529443562030792
          ],
          'descriptor': {shape: [1, 1, 5, 5], dataType: 'float32'}
        },
        'conv2dFilter': {
          'data': [
            0.3804761469364166, 0.5280312299728394, 0.21947036683559418,
            0.36689770221710205, 0.33974137902259827, 0.4200059771537781,
            0.3805030882358551, 0.19443586468696594, 0.5686976909637451
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'},
        }
      },
      'operators': [
        {
          'name': 'conv2d',
          'arguments': [{'input': 'conv2dInput'}, {'filter': 'conv2dFilter'}],
          'outputs': 'conv2dOutput'
        },
        {
          'name': 'hardSwish',
          'arguments': [{'input': 'conv2dOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            1.157502485501543, 0.9857435818773853, 0.9922408563279537,
            0.7272583864195519, 0.7742814812380979, 1.0964487730571852,
            0.7333530675289874, 0.7998542619888367, 1.2860601012485775
          ],
          'descriptor': {shape: [1, 1, 3, 3], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'conv2d default + gelu',
    'graph': {
      'inputs': {
        'conv2dInput': {
          'data': [
            0.6124474406242371,  0.8857858777046204,  0.13667134940624237,
            0.5645291209220886,  0.8965172171592712,  0.36792829632759094,
            0.6811466217041016,  0.0479511022567749,  0.33355462551116943,
            0.19882695376873016, 0.41167140007019043, 0.07934240251779556,
            0.4272463321685791,  0.535800576210022,   0.5910806059837341,
            0.28415432572364807, 0.4147258698940277,  0.026906268671154976,
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        {
          'name': 'gelu',
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    'name': 'convTranspose2d default + hardSwish',
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        {
          'name': 'hardSwish',
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      'expectedOutputs': {
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        {
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        {
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            0.9801966547966003, 0.06169835478067398, 0.3220160901546478,
            0.7498031854629517, 0.3930714726448059, 0.13811933994293213,
            0.28385090827941895, 0.4235861301422119, 0.1448512077331543
          ],
          'descriptor': {shape: [1, 2, 3, 3], dataType: 'float32'},
          'constant': true
        }
      },
      'operators': [
        {
          'name': 'convTranspose2d',
          'arguments': [
            {'input': 'convTranspose2dInput'},
            {'filter': 'convTranspose2dFilter'},
            {'options': {'inputLayout': 'nchw'}}
          ],
          'outputs': 'convTranspose2dOutput'
        },
        {
          'name': 'softmax',
          'arguments': [{'input': 'convTranspose2dOutput'}, {'axis': 1}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [
            0.49833576343872565, 0.4868008917870872,  0.5846997575195981,
            0.6440102325142313,  0.551181906978995,   0.4897745354808822,
            0.5547395504993903,  0.5345537346530161,  0.7474278654695712,
            0.7016867653522572,  0.5063253693672739,  0.48246072443639854,
            0.7623912436471291,  0.8061268489635616,  0.7996560653284985,
            0.506431947475152,   0.5613868238161465,  0.5802700289121353,
            0.7796113177719141,  0.7480226893035377,  0.5010695683288174,
            0.521090376342132,   0.6223909030394784,  0.6938916162243012,
            0.5905655851990261,  0.5016642365612743,  0.5131991082129128,
            0.4153002424804018,  0.35598976748576877, 0.44881809302100495,
            0.5102254645191179,  0.4452604495006097,  0.4654462653469838,
            0.2525721345304288,  0.29831323464774284, 0.4936746306327262,
            0.5175392755636015,  0.237608756352871,   0.19387315103643848,
            0.20034393467150155, 0.493568052524848,   0.43861317618385354,
            0.4197299710878647,  0.22038868222808597, 0.2519773106964624,
            0.4989304316711825,  0.4789096236578681,  0.37760909696052153,
            0.30610838377569893, 0.409434414800974
          ],
          'descriptor': {shape: [1, 2, 5, 5], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'batchNormalization options.axis=0  + softmax',
    'graph': {
      'inputs': {
        'bnInput': {
          'data': [-1, 0, 1, 2, 3, 4],
          'descriptor': {shape: [3, 1, 2], dataType: 'float32'}
        },
        'bnMean': {
          'data': [0, 3, 6],
          'descriptor': {shape: [3], dataType: 'float32'}
        },
        'bnVariance': {
          'data': [1.0, 1.5, 2.0],
          'descriptor': {shape: [3], dataType: 'float32'}
        }
      },
      'operators': [
        {
          'name': 'batchNormalization',
          'arguments': [
            {'input': 'bnInput'}, {'mean': 'bnMean'},
            {'variance': 'bnVariance'}, {'options': {'axis': 0}}
          ],
          'outputs': 'bnOutput'
        },
        {
          'name': 'softmax',
          'arguments': [{'input': 'bnOutput'}, {'axis': 1}],
          'outputs': 'output'
        }
      ],
      'expectedOutputs': {
        'output': {
          'data': [1, 1, 1, 1, 1, 1],
          'descriptor': {shape: [3, 1, 2], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'add + sub + mul + gather default',
    'graph': {
      'inputs': {
        'addA': {
          'data': [10],
          'descriptor': {shape: [], dataType: 'int32'},
          'constant': true
        },
        'addB': {
          'data': [20],
          'descriptor': {shape: [], dataType: 'int32'},
          'constant': true
        },
        'subB': {
          'data': [40],
          'descriptor': {shape: [], dataType: 'int32'},
        },
        'divA': {
          'data': [-20],
          'descriptor': {shape: [], dataType: 'int32'},
          'constant': true
        },
        'gatherInput': {
          'data': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
          'descriptor': {shape: [3, 4], dataType: 'float32'},
          'constant': true
        },
      },
      'operators': [
        {
          'name': 'add',
          'arguments': [{'a': 'addA'}, {'b': 'addB'}],
          'outputs': 'addOutput'
        },
        {
          'name': 'sub',
          'arguments': [{'a': 'addOutput'}, {'b': 'subB'}],
          'outputs': 'subOutput'
        },
        {
          'name': 'div',
          'arguments': [{'a': 'divA'}, {'b': 'subOutput'}],
          'outputs': 'divOutput'
        },
        {
          'name': 'gather',
          'arguments': [{'input': 'gatherInput'}, {'indices': 'divOutput'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [0.9, 1.0, 1.1, 1.2],
          'descriptor': {shape: [4], dataType: 'float32'}
        }
      }
    }
  },
  {
    'name': 'float16 graph with float32 input and output',
    'graph': {
      'inputs': {
        'input': {
          'data': [1, 2, 3, 4],
          'descriptor': {shape: [4], dataType: 'float32'}
        },
        'weight': {
          'data': [2],
          'descriptor': {shape: [], dataType: 'float16'},
          'constant': true
        }
      },
      'operators': [
        {
          'name': 'cast',
          'arguments': [{'input': 'input'}, {'type': 'float16'}],
          'outputs': 'castOutput',
        },
        {
          'name': 'add',
          'arguments': [{'a': 'castOutput'}, {'b': 'weight'}],
          'outputs': 'addOutput'
        },
        {
          'name': 'cast',
          'arguments': [{'input': 'addOutput'}, {'type': 'float32'}],
          'outputs': 'output'
        },
      ],
      'expectedOutputs': {
        'output': {
          'data': [3, 4, 5, 6],
          'descriptor': {shape: [4], dataType: 'float32'}
        }
      }
    }
  },
];

webnn_conformance_test(
    subgraphTests, buildAndExecuteGraph, getPrecisionTolerance);