function {
        signature {
          name: "inference_func"
          input_arg {
            name: "arg0"
            type: DT_FLOAT
          }
        }
        ret {
          key: "retval0"
          value: "arg0"
        }
      }
    }
  }
  signature_def: {
    key: "predict0"
    value: {
      inputs: {
        key: "inputs"
        value: {
          name: "input0"

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  auto* retval_task0 =
      TFE_TensorHandleCopyToDevice(retvals[0], ctx, local_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

          bytes.length);
      return padToLong();
    }

    @Override
    public long padToLong() {
      long retVal = (bytes[0] & 0xFF);
      for (int i = 1; i < Math.min(bytes.length, 8); i++) {
        retVal |= (bytes[i] & 0xFFL) << (i * 8);
      }
      return retVal;
    }

    @Override
    void writeBytesToImpl(byte[] dest, int offset, int maxLength) {

               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation* forward_op_, Tape* tape,
               const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }

// Execute the operation defined by 'op' and return handles to computed
// tensors in `retvals`.
//
// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//

  Status AddInput(AbstractTensorHandle* input) override;
  Status AddInputList(absl::Span<AbstractTensorHandle* const> inputs) override;
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override;
  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override;
  Status SetAttrInt(const char* attr_name, int64_t value) override;

        }
    }

    public static class NetrDfsEnumEx extends DcerpcMessage {

        @Override
        public int getOpnum () {
            return 0x15;
        }

        public int retval;
        public String dfs_name;
        public int level;
        public int prefmaxlen;
        public DfsEnumStruct info;
        public NdrLong totalentries;

                e.decode(_src);

            }
        }
    }
    public static class NetrDfsEnumEx extends DcerpcMessage {

        public int getOpnum() { return 0x15; }

        public int retval;
        public String dfs_name;
        public int level;
        public int prefmaxlen;
        public DfsEnumStruct info;
        public NdrLong totalentries;

        public NetrDfsEnumEx(String dfs_name,

        List<Map<String, Object>> docs = JsonPath.from(response).getList("data");
        int prevVal = 0;
        for (Map<String, Object> doc : docs) {
            int sortValue = Integer.parseInt(doc.get(sortField).toString());
            assertTrue(sortValue >= prevVal);
            prevVal = sortValue;
        }
    }

    @Test
    public void searchTestWithWildcard() throws Exception {

      auto metadata_arg = metadata_proto.add_args();
      metadata_arg->set_kind(
          tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);
      metadata_arg->set_dtype(dtype);
    }
    // Set up one dummy retval.
    metadata_proto.add_retvals();
    bool use_tuple_args = true;
    std::vector<tensorflow::tpu::ShardingAndIndex> arg_core_mapping;
    std::vector<std::vector<xla::Shape>> per_core_arg_shapes;