FilterFunc: func(obj interface{}) bool {
			if cast, ok := obj.(*corev1.ConfigMap); ok {
				return cast.Namespace == configMapNamespace && cast.Name == configMapName
			}
			if tombstone, ok := obj.(cache.DeletedFinalStateUnknown); ok {
				if cast, ok := tombstone.Obj.(*corev1.ConfigMap); ok {
					return cast.Namespace == configMapNamespace && cast.Name == configMapName
				}
			}

class GetDimSizeOfType<int dim> : NativeCodeCall<
  "GetScalarOfType(getElementTypeOrSelf($1), "
  "$0.getType().cast<RankedTensorType>().getDimSize(" # dim # "))">;

// Same as the above with i32 element type.
class GetDimSizeAsI32<int dim> : NativeCodeCall<
  "GetScalarOfType($_builder.getIntegerType(32), "
  "$0.getType().cast<RankedTensorType>().getDimSize(" # dim # "))">;

      if (operands.size() != 1 || updates.size() != 1) return failure();

      ShapedType operand_type = mlir::cast<ShapedType>(operands[0].getType());
      ShapedType indices_type = mlir::cast<ShapedType>(indices.getType());
      ShapedType updates_type = mlir::cast<ShapedType>(updates[0].getType());

      Value new_updates = updates[0];

      // Can only convert with static shaped scatter.

      {},
      // Nodes
      {
          {{"two"},
           "Const",
           {},
           {{"value", test::AsScalar<int32>(2)}, {"dtype", DT_INT64}}},
          {{"scale"},
           "Cast",
           {"two"},
           {{"SrcT", DT_INT64}, {"DstT", DT_FLOAT}}},
          {{"y"}, "Mul", {"x", "scale"}, {{"T", DT_FLOAT}}},
      });

  tensorflow::set_tf2_execution(true);

  auto attr = mlir::cast<DenseElementsAttr>(permutation_op.getValue());
  for (const auto& value : attr.getValues<APInt>())
    permutation.push_back(value.getSExtValue());

  // Check if we can fold transpose into the operation.
  if (fold_operands && fold_transpose_in_ops) {
    SmallVector<int64_t, 8> permutation;

    auto attr = mlir::cast<DenseElementsAttr>(permutation_op.getValue());

                if (getClass() == invocationHandler.getClass()) {
                    BuildOperationEmittingInvocationHandler cast = (BuildOperationEmittingInvocationHandler) invocationHandler;
                    return cast.delegate.equals(delegate);
                }
            }
            return false;
        }
    }

        /*
         * We have an Iterator<E> and want to cast it to
         * MultiExceptionListIterator. Because we're inside an
         * AbstractIteratorTester<E>, that's implicitly a cast to
         * AbstractIteratorTester<E>.MultiExceptionListIterator. The runtime
         * won't be able to verify the AbstractIteratorTester<E> part, so it's
         * an unchecked cast. We know, however, that the only possible value for

//
//	CAST() related errors.
//
// ////////////////////////////////////////////////////////////////////////////////////
func errCastFailed(err error) *s3Error {
	return &s3Error{
		code:       "CastFailed",
		message:    "Attempt to convert from one data type to another using CAST failed in the SQL expression.",
		statusCode: 400,
		cause:      err,
	}
}

  func.setType(FunctionType::get(func.getContext(), arg_types, result_types));
}

void HandleFuncOp(Operation* op) {
  auto func = llvm::cast<func::FuncOp>(op);
  UpdateFuncType(func);
}

// Handles cast op between the first convolution and the block argument.
LogicalResult HandleCast(TF::CastOp cast_op, ArrayRef<int64_t> new_shape) {
  auto cast_input = cast_op.getX();
  // Update input type.

           << "requires device ordinal from device " << logical_core_name
           << " to be present in 'tf.device.replicate' op";
  }
  llvm::StringRef tpu_device =
      tpu_replica.cast<ArrayAttr>()[replica_id].cast<StringAttr>().getValue();
  return tensorflow::GetDeviceOrdinalFromDeviceString(op->getLoc(), tpu_device,
                                                      &device_ordinal);
}