// a sign of a bug in an Analyzer.
func (s *Set) Encode() []byte {
	encoder := new(objectpath.Encoder)

	// TODO(adonovan): opt: use a more efficient encoding
	// that avoids repeating PkgPath for each fact.

	// Gather all facts, including those from imported packages.
	var gobFacts []gobFact

	s.mu.Lock()
	for k, fact := range s.m {
		if debug {
			log.Printf("%v => %s\n", k, fact)
		}

    rhs_remove_idx.insert(v);
  }

  // Gather shapes for output.
  for (auto v : ddn.lhs_batch_dimensions()) {
    output_shape.push_back(lhs_shape[v]);
  }

  // Batch dimension is gathered from the right side.
  if (output_shape.empty()) {
    for (auto v : ddn.rhs_batch_dimensions()) {
      output_shape.push_back(rhs_shape[v]);
    }
  }

  // Gather remaining dimensions.

  }];
}

def TFL_GatherOp : TFL_Op<"gather", [
    Pure,
    QuantizableResult,
    SameOperandsAndResultsScale,
    TFL_OperandHasAtleastRank<0, 1>,
    PredOpTrait<"params and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    DynamicRangeQuantizedOpInterface
  ]> {
  let summary = "Gather operator";

  let description = [{

		return true
	}

	c.queue.Forget(key)
	return true
}

func (c *nodeResourcesController) sync(ctx context.Context, driverName string) error {
	logger := klog.FromContext(ctx)

	// Gather information about the actual and desired state.
	slices := c.sliceStore.List()
	var driverResources []*resourceapi.ResourceModel
	c.mutex.RLock()
	if active, ok := c.activePlugins[driverName]; ok {

      : inner_op_result(inner_op_result), island_result(island_result) {}

  Value inner_op_result;
  Value island_result;
};

// This structure is used to gather the new operands and result of an island
// during merging.
struct IslandOperandsAndResults {
  llvm::SmallSetVector<Value, 8> operands;
  llvm::SmallVector<IslandResult> results;
};

  // followed by gather. This is used when the saved model import path is used
  // during which resources don't get frozen in the python layer.
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::TFDevice::CreateDecomposeResourceOpsPass());

  pass_manager->addPass(mlir::TF::CreateTFRegionControlFlowToFunctional());
}

// This is the later part of the conversion in isolation. This enables a caller

					}
					r.Shuffle(n, func(i, j int) { p[i], p[j] = p[j], p[i] })
					return encodePerm(p)
				}},
			}

			for _, test := range tests {
				t.Run(test.name, func(t *testing.T) {
					// Gather chi-squared values and check that they follow
					// the expected normal distribution given n!-1 degrees of freedom.
					// See https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test and

    // CHECK-DAG: [[READVAR:%.+]] = "tf.ReadVariableOp"([[VAR]])
    // CHECK: [[GATHER:%.+]] = "tf.GatherV2"([[READVAR]], [[INDEX]], [[ZERO]]) <{batch_dims = 0 : i64}> {_xla_outside_compilation = "0"} : (tensor<*xi32>, tensor<?xi32>, tensor<i64>) -> tensor<*xi32>
    // CHECK: return [[GATHER]]

//		uintptr_t  Max;
//	};
//
// The Context field will be zero to gather a traceback from the
// current program execution point. In this case, the traceback
// function will be called from C code.
//
// Otherwise Context will be a value previously returned by a call to
// the context function. The traceback function should gather a stack
// trace from that saved point in the program execution. The traceback

// Sorts the operations in the provided range to enforce dominance.
// This is useful after fusing / reorganizing Operations in a block and later
// needing to readjust the ordering to ensure dominance.
LogicalResult SortTopologically(Block::iterator begin, Block::iterator end) {
  Block* block = begin->getBlock();
  // Either sort from `begin` to end of block or both `begin` and
  // `end` should belong to the same block.
  assert(end == block->end() ||