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

1!important;flex-shrink:1!important}.justify-content-start{-ms-flex-pack:start!important;justify-content:flex-start!important}.justify-content-end{-ms-flex-pack:end!important;justify-content:flex-end!important}.justify-content-center{-ms-flex-pack:center!important;justify-content:center!important}.justify-content-between{-ms-flex-pack:justify!important;justify-content:space-between!important}.justify-content-around{-ms-flex-pack:distribute!important;justify-content:space-around!important}.align-...

1!important;flex-shrink:1!important}.justify-content-start{-ms-flex-pack:start!important;justify-content:flex-start!important}.justify-content-end{-ms-flex-pack:end!important;justify-content:flex-end!important}.justify-content-center{-ms-flex-pack:center!important;justify-content:center!important}.justify-content-between{-ms-flex-pack:justify!important;justify-content:space-between!important}.justify-content-around{-ms-flex-pack:distribute!important;justify-content:space-around!important}.align-...

transparent transparent}.pace-center-radar-secondary .pace .pace-activity::before{border-color:#6c757d transparent transparent}.pace-center-simple-secondary .pace{background:#fff;border-color:#6c757d}.pace-center-simple-secondary .pace .pace-progress{background:#6c757d}.pace-material-secondary .pace{color:#6c757d}.pace-corner-indicator-secondary .pace .pace-activity{background:#6c757d}.pace-corner-indicator-secondary .pace .pace-activity::after,.pace-corner-indicator-secondary .pace .pace-activity::before{border:5px...

                                        TF_Status* 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,
    std::array<TensorHandlePtr, num_replicas>* components, TF_Status* status);

// Helper to pack `num_replicas` TFE_TensorHandles into one parallel handle.
template <std::size_t num_replicas>

                    ->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()),