private:
  LogicalResult matchAndRewrite(Operation* op,
                                PatternRewriter& rewriter) const override {
    // Remove `_tpu_replicate` attributes on each operation first.
    if (op->hasAttr(tensorflow::kTPUReplicateAttr)) {
      op->removeAttr(tensorflow::kTPUReplicateAttr);
      return success();
    }

    // Remove TPU operations.
    if (isa<TF::TPUReplicateMetadataOp, TF::TPUCompilationResultOp,

    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_") ||
        !call_op->hasAttr(kQuantTraitAttrName)) {
      return failure();
    }

    absl::Status check_status;
    // TODO(b/270906404): Support weight-only gather for uniform quantized opset
    // in PTQ mode

             << " but got " << element_ty;
      });
      return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {

    # performance by avoiding unnecessary cloning
    if cloned_types is None:
        cloned_types = _CLONED_TYPES_CACHE

    original_type = field.type_
    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
        original_type = original_type.__pydantic_model__
    use_type = original_type
    if lenient_issubclass(original_type, BaseModel):

    if (mlir::isa<TF::ResourceType>(tensor_type.getElementType())) {
      return true;
    }
  }
  return false;
}

bool IsTPUOp(mlir::Operation* op) {
  return op->hasAttr(TF::kReplicationInfoAttr);
}

StringAttr GetReplicationAttr(mlir::Operation* op) {
  return op->getAttrOfType<StringAttr>(TF::kReplicationInfoAttr);
}

_API_MODULE = _sys.modules[__name__].bitwise
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
_current_module = _sys.modules[__name__]

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

# Load tensorflow-io-gcs-filesystem if enabled

          }
        }
      }
    });
  }
  // Remove `kSkipIslandOutlining` attributes.
  for (func::FuncOp func_op : getOperation().getOps<func::FuncOp>()) {
    if (func_op->hasAttr(mlir::TF::kSkipIslandOutlining)) {
      func_op->removeAttr(mlir::TF::kSkipIslandOutlining);
    }
  }
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>>

    return (
        _annotation_is_complex(annotation)
        or _annotation_is_complex(origin)
        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely

_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable
_current_module = _sys.modules[__name__]
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

// version 8 and above.
constexpr StringRef kUsesShapePolymorphismAttr = "jax.uses_shape_polymorphism";

bool IsInLiftedFunc(Operation* op) {
  if (op == nullptr) return false;
  return op->getParentOfType<func::FuncOp>()->hasAttr(kFusedFunctionAttr);
}

bool IsInStableHloOpRegion(Operation* op) {
  if (op == nullptr) return false;
  auto parent_op = op->getParentOp();
  return parent_op != nullptr && stablehlo::IsStablehloOp(parent_op);
}