}
  },
  "customizations": {
      "vscode": {
          "settings": {
              "java.server.launchMode": "Standard"
          },
          "extensions": [
              "vscjava.vscode-java-pack",
              "vscjava.vscode-gradle"
          ]
      }
  }

                    ->DebugString(),
                " as input to a parallel operation. First pack non-parallel "
                "tensors for each device into a parallel tensor explicitly.")
                .c_str());
        return absl::nullopt;
      }
    } else {
      parallel_inputs.push_back(absl::get<ParallelTensor*>(input));
    }
  }
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>

    return 1 == (1 & (filter >> c));
  }

  // This is all essentially copied from ImmutableSet, but we have to duplicate because
  // of dependencies.

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing

    return 1 == (1 & (filter >> c));
  }

  // This is all essentially copied from ImmutableSet, but we have to duplicate because
  // of dependencies.

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing

	// Check if we can use USTAR prefix/suffix splitting.
	var namePrefix string
	if prefix, suffix, ok := splitUSTARPath(hdr.Name); ok {
		namePrefix, hdr.Name = prefix, suffix
	}

	// Pack the main header.
	var f formatter
	blk := tw.templateV7Plus(hdr, f.formatString, f.formatOctal)
	f.formatString(blk.toUSTAR().prefix(), namePrefix)
	blk.setFormat(FormatUSTAR)
	if f.err != nil {

        TFE_DeleteTensorHandle(outputs[i]);
      }
    }
    return status.status;
  }

  tensorflow::Status Pack(absl::Span<ImmediateExecutionTensorHandle*> handles,
                          ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    *result = tensorflow::unwrap(device_.pack(context_,
                                              tensorflow::wrap(handles.data()),

      Multiply(context.get(), second_combined_value.get(),
               second_negative_one.get(), status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Un-pack the parallel tensor to verify that the operation was
  // successful. The resulting structure should be:
  //   second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}.
  std::array<TensorHandlePtr, 2> second_components;

      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);
      tensorflow::ImmediateExecutionTensorHandle* result;
      status->status = custom_device_handle->device()->Pack(

                   &FloatDeallocator, nullptr),
      TF_DeleteTensor);
  return TensorHandlePtr(TFE_NewTensorHandle(tensor.get(), status));
}

// Helper to un-pack `num_replicas` TFE_TensorHandles from one parallel handle.
template <std::size_t num_replicas>
void ExtractPerDeviceValues(
    TFE_Context* context, TFE_TensorHandle* input,

  // custom device.
  //
  // Many devices will want to simply return an "unimplemented" status
  // here. This is the default behavior if `pack` is null when passed to
  // TFE_RegisterCustomDevice.
  TFE_TensorHandle* (*pack)(TFE_Context* context, TFE_TensorHandle** handles,
                            int num_handles, TF_Status* s,
                            void* device_info) = nullptr;