# CHECK: tf_executor.fetch %[[output]] : tensor<*xi64>

node {
  name: "serilaized"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: ""
      }
    }
  }
}
node {

meta_graphs: {
  meta_info_def: {
    tags: ["serve", "tpu"]
  }
  graph_def: {
    node: {
      name: "input0"
      op: "Placeholder"
      attr: {
        key: "dtype"
        value: {
          type: DT_STRING
        }
      }
    }
    node: {
      name: "input1"
      op: "Placeholder"
      attr: {
        key: "dtype"
        value: {
          type: DT_INT32
        }
      }
    }

    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/ParseExampleV2/sparse_keys"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {

          name: "serialized"
          type: DT_STRING
        }
        input_arg {
          name: "names"
          type: DT_STRING
        }
        input_arg {
          name: "sparse_keys"
          type: DT_STRING
          number_attr: "Nsparse"
        }
        input_arg {
          name: "dense_keys"
          type: DT_STRING
          number_attr: "Ndense"
        }

  } else {
    EXPECT_TRUE(absl::StrContains(ctx.status().ToString(), expected_output))
        << ctx.status();
  }
}

TEST(ScalarSummaryOpTest, SimpleFloat) {
  int vectorSize = 3;
  Tensor tags(DT_STRING, {vectorSize});
  Tensor values(DT_FLOAT, {vectorSize});
  tags.vec<tstring>()(0) = "tag1";
  tags.vec<tstring>()(1) = "tag2";
  tags.vec<tstring>()(2) = "tag3";
  values.vec<float>()(0) = 1.0f;

        shape {
        }
      }
    }
  }
}
node {
  name: "Const1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
        }
        string_val: ""
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {

        shape {
          dim {
            size: 3
          }
        }
      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 3
          }
        }
        string_val: ""
        string_val: ""
        string_val: ""

["tfdtype$DT_FLOAT", "tfdtype$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>,...

namespace tensorflow {
namespace {

Graph* BM_ScalarSummaryOp(TensorShape shape, std::string tag, float value) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor tags(DT_STRING, shape);
  Tensor values(DT_FLOAT, shape);
  for (int i = 0; i < tags.NumElements(); ++i) {
    tags.flat<tstring>()(i) = tag;
    values.flat<float>()(i) = value;
  }
  Node* ret;

    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;
    case tflite::TensorType_UINT64:
      return tensorflow::DT_UINT64;