able to grab the feed and see the feed's actual URL in
the link rel="self", but the default value for that drops
the :port from the URL, and I cannot for the life of me
figure out how to get the Atom generator deep inside
django not to do that, or even where it is doing that,
or even what code is running to generate the Atom feed.
(I thought I knew but I added some assert False statements

    // This just selects the default in all cases where DeviceIndex feeds into
    // tf.Case. This could be enhanced to have some sort of policy in the
    // future.
    OpBuilder b(op);
    RankedTensorType type = RankedTensorType::get({}, b.getIntegerType(32));
    int index = op.getDeviceNames().size();
    for (auto use : op.getOperation()->getUsers()) {
      // Skip if it doesn't feed into case. Alternatively this could always

  }

  // Don't prune if the condition block argument has any user.
  if (!cond_block_argument.use_empty()) return false;

  // Don't prune if `body_yield_operand` has more than one use (that would mean
  // it feeds into another op apart from `Yield`).
  if (!body_yield_operand.hasOneUse()) return false;

  // Don't prune if any other result of the candidate op is used.
  for (Value candidate_result : candidate_op->getResults()) {

    }
    i += Character.charCount(codePoint)
  }
}

/**
 * Returns a substring of `input` on the range `[pos..limit)` with the following
 * transformations:
 *
 *  * Tabs, newlines, form feeds and carriage returns are skipped.
 *
 *  * In queries, ' ' is encoded to '+' and '+' is encoded to "%2B".
 *
 *  * Characters in `encodeSet` are percent-encoded.
 *

		sentErr := false
		sendErr := func(err error) {
			if !sentErr {
				errch <- err
				sentErr = true
			}
		}

		// Copy our data to gzipWriter, which compresses it to
		// gzipWriter, which feeds it to bodyReader.
		if _, err := io.Copy(gzipWriter, dataReader); err != nil && err != io.ErrClosedPipe {
			sendErr(err)
		}
		if err := gzipWriter.Close(); err != nil && err != io.ErrClosedPipe {
			sendErr(err)

  llvm::SmallDenseSet<mlir::StringAttr, 4> ops_to_preserve_ids_;
};

// Checks if a tf_executor.Graph can be pruned.
// For TensorFlow V1.0 compatibility: when importing a graph without providing
// feeds/fetches/targets we should not attempt to prune. The best approximation
// here is to check if the graph is of the "main" function and does not have the
// "tf.entry_function" attribute defined.
bool CanPruneGraph(func::FuncOp func) {

		Status: metav1.StatusSuccess,
	}})
	metrics.IncStreamTranslatorRequest(req.Context(), strconv.Itoa(http.StatusOK))
}

// translatorSizeQueue feeds the size events from the WebSocket
// resizeChan into the SPDY client input. Implements TerminalSizeQueue
// interface.
type translatorSizeQueue struct {
	resizeChan chan remotecommand.TerminalSize
}

  // inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  // For V1 compatibility, we process a module where the graph does not have
  // feeds and fetched. We extract first the TPU computation in a submodule,
  // where it'll be in a function with args and returned values, much more
  // like a TF v2 module. We can then run the usual pipeline on this nested

}

TEST_F(CApiColocationTest, Proto_ColocateWith) {
  SetViaProto(desc_, {"loc:@feed2"});
  TF_ColocateWith(desc_, feed1_);
  FinishAndVerify(desc_, {"loc:@feed1", "loc:@feed2"});
}

TEST_F(CApiColocationTest, ColocateWith_ColocateWith) {
  TF_ColocateWith(desc_, feed1_);
  TF_ColocateWith(desc_, feed2_);
  FinishAndVerify(desc_, {"loc:@feed1", "loc:@feed2"});
}

}

// Returns a TPUPartitionedInput op connected to a `tf_device.cluster_func`
// operand value if it has an XLA sharding. If value is a resource type then
// TPUPartitionedInput op will be connected to a ReadVariable op that feeds into
// a `tf_device.cluster_func`.
mlir::Operation* GetXlaShardingFromOperand(Value value) {
  Value value_to_visit = value;
  if (auto read_var = value_to_visit.getDefiningOp<mlir::TF::ReadVariableOp>())