{
  "op": "ai.onnx.Identity",
  "cases": [
    {
      "name": "int16_copy_boundaries",
      "inputs": {
        "x": { "dtype": "int16", "shape": [4], "data": { "kind": "values", "values": [-32768, -1, 0, 32767] } }
      },
      "outputs": {
        "y": {
          "dtype": "int16",
          "shape": [4],
          "tolerance": 0,
          "data": { "kind": "values", "values": [-32768, -1, 0, 32767] }
        }
      }
    },
    {
      "name": "float32_copy",
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [3, 7],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [3, 7], "tolerance": 0.000001 } }
    },
    {
      "name": "ort_float_2x2",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType"
      },
      "inputs": {
        "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [2, 2], "tolerance": 0 } }
    },
    {
      "name": "onnx_backend_nchw_copy",
      "provenance": {
        "source": "cmake/external/onnx/onnx/backend/test/data/node/test_identity",
        "test": "test_identity"
      },
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 2], "tolerance": 0 } }
    },
    {
      "name": "float32_scalar_copy",
      "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": ["NaN"] } } },
      "outputs": { "y": { "dtype": "float32", "shape": [], "tolerance": 0.000001, "allowNaN": true } }
    },
    {
      "name": "float16_copy",
      "inputs": {
        "x": {
          "dtype": "float16",
          "shape": [2, 3],
          "data": { "kind": "values", "values": [-5.0, -1.0, 0.0, 1.0, 5.0, 9.0] }
        }
      },
      "outputs": { "y": { "dtype": "float16", "shape": [2, 3], "tolerance": 0.001 } }
    },
    {
      "name": "uint32_copy",
      "inputs": {
        "x": {
          "dtype": "uint32",
          "shape": [2, 3],
          "data": { "kind": "values", "values": [0, 1, 2, 4294967295, 17, 99] }
        }
      },
      "outputs": { "y": { "dtype": "uint32", "shape": [2, 3], "tolerance": 0 } }
    },
    {
      "name": "bool_exact_copy",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType",
        "notes": "Same tensor-copy behavior as ORT's numeric fixture, using ONNX-valid bool payloads."
      },
      "inputs": {
        "x": { "dtype": "bool", "shape": [2, 3], "data": { "kind": "values", "values": [0, 1, 1, 0, 1, 0] } }
      },
      "outputs": {
        "y": {
          "dtype": "bool",
          "shape": [2, 3],
          "data": { "kind": "values", "values": [0, 1, 1, 0, 1, 0] },
          "tolerance": 0
        }
      }
    },
    {
      "name": "ort_int32_copy_dtype_path",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType",
        "notes": "Same tensor-copy behavior as ORT's float fixture, using supported native int32 storage."
      },
      "inputs": {
        "x": { "dtype": "int32", "shape": [2, 2], "data": { "kind": "values", "values": [-3, 0, 7, 2147483647] } }
      },
      "outputs": { "y": { "dtype": "int32", "shape": [2, 2], "tolerance": 0 } }
    },
    {
      "name": "ort_int8_copy_edge_values",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType",
        "notes": "Same tensor-copy behavior as ORT's float fixture, using logical int8 edge values."
      },
      "inputs": {
        "x": {
          "dtype": "int8",
          "shape": [2, 4],
          "data": { "kind": "values", "values": [-128, -5, -1, 0, 1, 5, 100, 127] }
        }
      },
      "outputs": { "y": { "dtype": "int8", "shape": [2, 4], "tolerance": 0 } }
    },
    {
      "name": "ort_uint8_copy_edge_values",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType",
        "notes": "Same tensor-copy behavior as ORT's float fixture, using logical uint8 edge values."
      },
      "inputs": {
        "x": {
          "dtype": "uint8",
          "shape": [2, 4],
          "data": { "kind": "values", "values": [0, 1, 2, 127, 128, 200, 254, 255] }
        }
      },
      "outputs": { "y": { "dtype": "uint8", "shape": [2, 4], "tolerance": 0 } }
    },
    {
      "name": "ort_float16_copy_nonfinite_values",
      "provenance": {
        "source": "onnxruntime/test/providers/cpu/tensor/identity_op_test.cc",
        "test": "Identity.FloatType",
        "notes": "Same tensor-copy behavior as ORT's float fixture, using float16 nonfinite payloads."
      },
      "inputs": {
        "x": {
          "dtype": "float16",
          "shape": [2, 4],
          "data": { "kind": "values", "values": ["NaN", "-Infinity", 0.0, 0.0, 1.0, -2.0, 65504.0, "Infinity"] }
        }
      },
      "outputs": { "y": { "dtype": "float16", "shape": [2, 4], "tolerance": 0, "allowNaN": true } }
    },
    {
      "name": "zero_sized_copy",
      "inputs": { "x": { "dtype": "float32", "shape": [2, 0, 3], "data": { "kind": "values", "values": [] } } },
      "outputs": { "y": { "dtype": "float32", "shape": [2, 0, 3], "tolerance": 0.000001 } }
    },
    {
      "name": "onnx_backend_clip_default_inbounds_expanded",
      "provenance": {
        "source": "cmake/external/onnx/onnx/backend/test/data/node/test_clip_default_inbounds_expanded",
        "notes": "The official expanded Clip default-inbounds function lowers to Identity for this node fixture."
      },
      "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } },
      "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0 } }
    },
    {
      "name": "onnx_backend_clip_default_int8_inbounds_expanded",
      "provenance": {
        "source": "cmake/external/onnx/onnx/backend/test/data/node/test_clip_default_int8_inbounds_expanded",
        "notes": "The official expanded Clip default-inbounds function lowers to Identity for this node fixture."
      },
      "inputs": { "x": { "dtype": "int8", "shape": [3], "data": { "kind": "values", "values": [-1, 0, 1] } } },
      "outputs": { "y": { "dtype": "int8", "shape": [3], "tolerance": 0 } }
    },
    {
      "name": "f16_vec4_copy_2x4",
      "inputs": {
        "x": {
          "dtype": "float16",
          "shape": [2, 4],
          "data": { "kind": "values", "values": [1.0, -2.0, 3.5, -4.5, 5.0, -6.0, 7.25, -8.75] }
        }
      },
      "outputs": { "y": { "dtype": "float16", "shape": [2, 4], "tolerance": 0 } }
    },
    {
      "name": "scalar_copy_dispatch_clamp",
      "provenance": {
        "notes": "Odd numel forces the scalar copy variant; WORKGROUP_SIZE=4 makes ceil(numel/WG)=65536 exceed the 65535 workgroups-per-dimension limit, pinning the min(...,65535) dispatch clamp + grid-stride loop (some threads copy two elements)."
      },
      "tunables": { "WORKGROUP_SIZE": 4 },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [262141],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [262141], "tolerance": 0 } }
    },
    {
      "name": "int32_numel2_scalar_copy",
      "inputs": {
        "x": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [-2147483648, 2147483647] } }
      },
      "outputs": { "y": { "dtype": "int32", "shape": [2], "tolerance": 0 } }
    },
    {
      "name": "f16_numel3_scalar_copy_edge_values",
      "inputs": {
        "x": {
          "dtype": "float16",
          "shape": [3],
          "data": { "kind": "values", "values": ["Infinity", "-Infinity", 65504.0] }
        }
      },
      "outputs": {
        "y": {
          "dtype": "float16",
          "shape": [3],
          "data": { "kind": "values", "values": ["Infinity", "-Infinity", 65504.0] },
          "tolerance": 0,
          "allowNaN": false
        }
      }
    },
    {
      "name": "empty_zero_in_middle_dim",
      "inputs": { "x": { "dtype": "float32", "shape": [3, 0, 5], "data": { "kind": "values", "values": [] } } },
      "outputs": { "y": { "dtype": "float32", "shape": [3, 0, 5], "tolerance": 0 } }
    }
  ]
}