// uses `get_next` to get the next value and calls `reduce_func`.  `body_args`
// is used as pass through of state values for else branch.  `dataset_types` is
// used for constructing the CallOp for `reduce_func`.
IfRegionOp CreateOptionalDatasetIf(
    OpBuilder builder, ReduceDatasetOp reduce_dataset, FuncOp reduce_func,
    IteratorGetNextAsOptionalOp get_next, OptionalHasValueOp optional_has_value,

          op, {if_op.then_function(), if_op.else_function()}, if_op.getInput());
    } else if (auto case_op = dyn_cast<TF::CaseOp>(op)) {
      SmallVector<func::FuncOp, 4> branches;
      case_op.get_branch_functions(branches);
      result =
          CanonicalizeFunctionalIfCase(case_op, branches, case_op.getInput());
    } else if (auto while_op = dyn_cast<TF::WhileOp>(op)) {

	// Create new entry for this inline
	inlinedFn := base.Ctxt.InlTree.InlinedFunction(inlIdx)
	callXPos := base.Ctxt.InlTree.CallPos(inlIdx)
	callPos := base.Ctxt.InnermostPos(callXPos)
	absFnSym := base.Ctxt.DwFixups.AbsFuncDwarfSym(inlinedFn)
	ic := dwarf.InlCall{
		InlIndex:  inlIdx,
		CallPos:   callPos,
		AbsFunSym: absFnSym,
		Root:      parCallIdx == -1,
	}
	dwcalls.Calls = append(dwcalls.Calls, ic)

// that result if so. Otherwise, it will clone and convert the callee function,
// and performs stack ops decomposition on it.
template <typename CallOp>
LogicalResult HandlePartitionedCallOp(
    CallOp call, func::FuncOp callee, ModuleOp module,
    const llvm::SmallDenseMap<Value, Value>& data_var_to_size_var,
    llvm::StringMap<PartitionedCallStackOpsInfo>*

					}
					newTLSSidecarDestinationRule(t, apps.External.All, "MUTUAL", tc.drSelector, tc.credentialToUse,
						tc.from.Config().Namespace)
					callOpt := newTLSSidecarCallOpts(apps.External.All[0], host, tc.expectedResponse)
					tc.from[0].CallOrFail(t, callOpt)
				})
			}
		})
}

// Authorize only specific sidecars in the namespace with secret listing permissions.

  // begin[i].
  //
  // If slice_size[i] < -1 then executing the slice will throw an error, and we
  // don't do anything here.  We've already filtered these cases out in
  // IsRewritableSlice.

  if (absl::c_all_of(slice_inputs.size_as_vector,
                     [](int64_t i) { return i >= 0; })) {
    *size = slice_inputs.size;
    return absl::OkStatus();
  }

  Output input_shape =

  }

  for (auto pair : llvm::zip(new_op.getRegions(), case_op.getRegions())) {
    std::get<0>(pair)->takeBody(*std::get<1>(pair));
  }
  case_op.replaceAllUsesWith(
      new_op.getResults().take_front(case_op.getNumResults()));
  case_op.erase();
  return success();
}

template <typename CallOp>
LogicalResult HandlePartitionedCallOp(
    CallOp call, func::FuncOp callee, ModuleOp module,

		// and spilled to temporary variables, which can be captured by
		// the wrapper function.

		stmtPos := base.Pos
		callPos := base.Pos

		as := ir.NewAssignListStmt(callPos, ir.OAS2, make([]ir.Node, len(argps)), make([]ir.Node, len(argps)))
		for i, argp := range argps {
			arg := *argp

			pos := callPos
			if ir.HasUniquePos(arg) {
				pos = arg.Pos()
			}

			// tmp := arg

      TF::UniformQuantizedClipByValueOp>();
  target.addDynamicallyLegalOp<TF::CastOp>([](Operation *op) {
    auto cast_op = llvm::dyn_cast<TF::CastOp>(op);
    return !IsTFQintType(cast_op.getSrcT()) && !IsTFQintType(cast_op.getDstT());
  });

  RewritePatternSet patterns(ctx);
  PopulateLegalizeTfQuantizationPatterns(ctx, &patterns);

  %case_op = "tf.Case"(%arg0, %tl) {branches = [@branch_0, @branch_1, @branch_2], is_stateless = false}
    : (tensor<i32>, tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK: "tf.Slice"