AbstractTensorHandle* AggregateGradients(
      absl::Span<AbstractTensorHandle* const> gradient_tensors) const override;

  // Calls the passed-in backward function.
  // op_type is the op's name provided in RecordOperation.
  absl::Status CallBackwardFunction(
      const string& op_type, GradientFunction* gradient_function,
      const std::vector<int64_t>& unneeded_gradients,
      absl::Span<AbstractTensorHandle* const> output_gradients,

absl::Status SetAttrFunctionList(AbstractOperation*, const char* attr_name,
                                 absl::Span<const AbstractOperation*> values,
                                 ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
absl::Status Execute(AbstractOperation*, AbstractContext*,
                     absl::Span<AbstractTensorHandle*> retvals,
                     int* num_retvals, ForwardOperation*, Tape*,

  // on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle* const> outputs,
                       GradientFunction* gradient_function,

        }
        
        public long getThroughput() {
            return bytesSent.get() + bytesReceived.get();
        }
    }
    
    public void recordOperation(String channelId, long bytes, long latency, boolean success) {
        ChannelStatistics stats = channelStats.computeIfAbsent(channelId, 
            k -> new ChannelStatistics());
            

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,
                              /*targets=*/{neg_output},
                              /*sources=*/inputs,