group_annotation = absl::StrCat(parallel_group_attr.getValue().str(),
                                        ",", group_annotation);
      }
      for (auto& op : execute_island.GetBody()) {
        op.setAttr(TF::kParallelExecAnnotation,
                   builder->getStringAttr(group_annotation));
      }
    }

    executes.push_back(execute_island);
  }
}

void CreateIslandsFromParallelExecute(

    new_float_func.setType(FunctionType::get(
        getContext(), TypeRange{ValueRange{new_float_func_args}},
        new_float_func.getResultTypes()));
    symbol_table.insert(new_float_func);

    op->setAttr("f", SymbolRefAttr::get(rewriter.getContext(),
                                        new_float_func.getName()));

    return success();
  }

  LogicalResult matchAndRewrite(TF::PartitionedCallOp op,

    auto min_consumer = b.getNamedAttr("min_consumer", b.getI32IntegerAttr(0));
    auto bad_consumers =
        b.getNamedAttr("bad_consumers", b.getI32ArrayAttr({1, 2, 3, 4}));

    module->setAttr("tf.versions",
                    b.getDictionaryAttr(llvm::ArrayRef<mlir::NamedAttribute>(
                        {producer, min_consumer, bad_consumers})));

    return run_status_;
  }

  Status run_status_;

            continue;
          }
          auto new_attr = mlir::DenseElementsAttr::get(
              llvm::cast<mlir::ShapedType>(attr.getType()), rand_val);
          inst.setAttr(attr_id, new_attr);
        }
      }
    }
  }
  return module_or;
}

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToSplattedMlirTranslateFunction(

          /*type=*/mlir::TypeAttr::get(value_attr.getType()),
          /*is_mutable=*/nullptr);
      op->setAttr(
          kTfSavedModelExportedNamesAttr,
          builder.getStrArrayAttr(object_names.GetExportedNames(node_id)));
    }
  }
  AdjustBoundInputArgTypes(module);
  module->setAttr("tf_saved_model.semantics", builder.getUnitAttr());
  SortSavedModelModule(module);

        }

        rewriter.setInsertionPoint(quantizing_op);
        Operation* float_op = rewriter.clone(*quantizing_op);
        quantizing_op->setAttr(kDebugModeOpQuantAttrName,
                               rewriter.getUnitAttr());
        float_op->setAttr(kDebugModeOpFloatAttrName, rewriter.getUnitAttr());
        RewireFloatModelBackbone(quantizing_op, float_op);
        return success();
      }

  llvm::SmallVector<mlir::Type, 4> output_types(num_split, output_type);
  *split_op = builder->create<mlir::TF::SplitOp>(
      location, output_types, split_dimension_op.getOutput(), src_input);
  (*split_op)->setAttr(
      kNumSplitAttr,
      builder->getIntegerAttr(builder->getIntegerType(32), num_split));
  return mlir::success();
}

// Creates a tf::ConcatOp that merges `input` values in `concat_dimension`.

  Operation* parent_op = op->getParentOp();
  if (!parent_op) return false;
  return HasOutsideCompilationAncestor(parent_op);
}

Operation* ApplyXlaHostTransferAttr(Operation* op, OpBuilder& builder) {
  op->setAttr("_xla_has_host_transfer", builder.getBoolAttr(true));
  return op;
}

// Creates a tf._XlaSendFromHost or tf._XlaSendFromHostV2 op. If device ordinal
// is present, a tf._XlaSendFromHostV2 op is created instead.

  op->replaceAllUsesWith(flex_op);
  op->erase();
  return true;
}

// Sets the "no_fallback" attribute.
Value SetNoFallbackAttr(PatternRewriter &rewriter, Value val) {
  val.getDefiningOp()->setAttr(kNoFallbackAttr, rewriter.getUnitAttr());
  return val;
}

// Returns true if the attr is a float attribute and be equal to value.
static bool FloatValueEquals(const Attribute &attr, double value) {

        if (auto device = result->DeviceForResource(output)) {
          LLVM_DEBUG(llvm::dbgs() << " Setting device = " << *device << "\n");
          identity->setAttr(kDeviceAttr, builder.getStringAttr(*device));
        }
      }
    } else if (auto while_region = dyn_cast<WhileRegionOp>(op)) {
      // For WhileRegion, do local analysis prior to visiting the attached