attr: {
    key: "Tcaptured"
    value: {
      list: {
        type: [DT_INT64]
      }
    }
  }
  attr: {
    key: "Tin"
    value: {
      list: {
        type: [DT_INT64]
      }
    }
  }
  attr: {
    key: "Tout"
    value: {
      list: {
        type: [DT_INT64]
      }
    }
  }
  attr: {
    key: "batch_timeout_micros"
    value: {

        string_val: "value"
      }
    }
  }
}
node {
  name: "Const_2"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT64
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT64
        tensor_shape {
        }
        int64_val: -1
      }
    }
  }
}
node {
  name: "result"
  op: "ParseExample"
  input: "serilaized"

          }
        }
      }
    }
  }
}
node {
  name: "Const0"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT64
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT64
        tensor_shape {
        }
        int64_val: -1
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {

  OpDef op_def = reg->op_def;
  NodeDef def;
  TF_CHECK_OK(NodeDefBuilder("dummy", &op_def)
                  .Attr("type", DT_INT8)
                  .Attr("T", DT_INT64)
                  .Input(FakeInput(DT_INT64))
                  .Finalize(&def));
  shape_inference::InferenceContext c(0, def, op_def, {S({3, 4})}, {}, {}, {});
  std::vector<shape_inference::ShapeHandle> input_shapes;

"tfdtype$DT_FLOAT"], dense_shapes = [#tf_type.shape<>, #tf_type.shape<>], device = "", num_sparse = 2 : i64, ragged_split_types = [], ragged_value_types = [], resultSegmentSizes = array<i32: 2, 2, 2, 2, 0, 0>, sparse_types = ["tfdtype$DT_STRING", "tfdtype$DT_INT64"]} : (tensor<32x!tf_type.string>, tensor<0x!tf_type.string>, tensor<2x!tf_type.string>, tensor<2x!tf_type.string>, tensor<0x!tf_type.string>, tensor<0xf32>, tensor<0xf32>) -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>,...

node {
  name: "x"
  op: "VarHandleOp"
  device: "/CPU:0"
  attr {
    key: "container"
    value {
      s: "a"
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_INT64
    }
  }
  attr {
    key: "shape"
    value {
      shape {
      }
    }
  }
  attr {
    key: "shared_name"
    value {
      s: "x"
    }
  }
}
node {

    case DT_INT16:
      *type = builder.getIntegerType(16);
      return absl::OkStatus();
    case DT_INT32:
      *type = builder.getIntegerType(32);
      return absl::OkStatus();
    case DT_INT64:
      *type = builder.getIntegerType(64);
      return absl::OkStatus();
    case DT_UINT8:
      *type = builder.getIntegerType(8, /*isSigned=*/false);
      return absl::OkStatus();
    case DT_UINT16:

// CHECK-NEXT:   value {
// CHECK-NEXT:     i: 0
// CHECK-NEXT:   }
// CHECK-NEXT: }
// CHECK-NEXT: attr {
// CHECK-NEXT:   key: "Tdense"
// CHECK-NEXT:   value {
// CHECK-NEXT:     list {
// CHECK-NEXT:       type: DT_INT64
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT: }
// CHECK-NEXT: attr {
// CHECK:        key: "dense_shapes"
// CHECK-NEXT:   value {
// CHECK-NEXT:     list {
// CHECK-NEXT:       shape {

    case tflite::TensorType_INT32:
      return tensorflow::DT_INT32;
    case tflite::TensorType_UINT32:
      return tensorflow::DT_UINT32;
    case tflite::TensorType_INT64:
      return tensorflow::DT_INT64;
    case tflite::TensorType_STRING:
      return tensorflow::DT_STRING;
    case tflite::TensorType_UINT8:
      return tensorflow::DT_UINT8;
    case tflite::TensorType_UINT16:
      return tensorflow::DT_UINT16;

  attr {
    key: "ragged_value_types"
    value {
      list {
      }
    }
  }
  attr {
    key: "sparse_types"
    value {
      list {
        type: DT_STRING
        type: DT_INT64
      }
    }
  }
}
versions {
  producer: 175