{
      Block &entry_block = outlined_func.getBody().front();
      auto loc = outlined_func.getLoc();
      for (Value operand : operands) {
        BlockArgument newArg = entry_block.addArgument(operand.getType(), loc);
        replaceAllUsesInRegionWith(operand, newArg, outlined_func.getBody());
      }
    }

    // The function is in place in the nested module, create a call and yield in
    // the original island.

  for (auto res : llvm::enumerate(call_op.getResultTypes())) {
    Type res_type = res.value();
    if (mlir::dyn_cast<TFRTensorType>(res_type)) {
      Value new_res = new_op->getResult(res.index());
      auto casted = rewriter.create<CastOp>(loc, res_type, new_res);
      new_results.push_back(casted.getOut());
    } else if (auto list_type =
                   mlir::dyn_cast<TFRTensorListType>(res.value())) {

				hasErr = true
			}
			continue
		}

		// declare new variable
		obj := NewVar(ident.Pos(), check.pkg, name, nil)
		lhsVars[i] = obj
		if name != "_" {
			newVars = append(newVars, obj)
		}
		check.recordDef(ident, obj)
	}

	// create dummy variables where the lhs is invalid
	for i, obj := range lhsVars {
		if obj == nil {

  mlir::FunctionType func_type = main_func.getFunctionType();

  int num_args = func_type.getNumInputs();
  xla_input_shapes->reserve(num_args);

  std::vector<xla::Shape> individual_arg_shapes;
  individual_arg_shapes.reserve(num_args);
  for (int i = 0; i < num_args; ++i) {
    individual_arg_shapes.emplace_back();
    xla::Shape& xla_shape = individual_arg_shapes.back();

                                  available_labels);
      new_lhs.append(new_labels);
      i += 2;
    }
  }

  std::string new_rhs, new_rhs_labels;
  for (int i = 0; i < rhs.size(); ++i) {
    const char label = rhs[i];
    if (std::isalpha(label)) {
      new_rhs.push_back(label);
    } else {
      // Encounter ellipsis: generate unnamed labels then insert to the new
      // labels.

        const int num_args = input_permutation->size();
        std::vector<bool> const_args(num_args);
        TF_RETURN_IF_ERROR(
            BackwardsConstAnalysis(**subgraph, &const_args,
                                   /*compile_time_const_nodes=*/nullptr, flr));

        DataTypeVector arg_types(num_args);
        TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types));

from starlette.requests import HTTPConnection, Request
from starlette.responses import Response
from starlette.websockets import WebSocket
from typing_extensions import Annotated, get_args, get_origin

multipart_not_installed_error = (
    'Form data requires "python-multipart" to be installed. \n'
    'You can install "python-multipart" with: \n\n'
    "pip install python-multipart\n"
)

      auto num_args_attr = IntegerAttr::get(IntegerType::get(context, 64), 1);
      attrs.push_back(
          NamedAttribute(StringAttr::get(context, "num_args"), num_args_attr));

      // Fused conv operands are input, filter, args and host args. Here, bias
      // input of the BiasAdd op. Host args corresponds to conv_input_scale and

    data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 9.99999974E-5 : f32, explicit_paddings = [], filter_format = "HWIO", fused_ops = ["BiasAdd", "Relu"], leakyrelu_alpha = 2.000000e-01 : f32, num_args = 2 : i64, operandSegmentSizes = array<i32: 1, 1, 2, 2>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true

		return fmt.Errorf("patch %v/%v/%v: %v", us.GroupVersionKind(), us.GetNamespace(), us.GetName(), err)
	}
	return nil
}

func (d *DeploymentController) HandleTagChange(newTags sets.String) {
	for _, gw := range d.gateways.List(metav1.NamespaceAll, klabels.Everything()) {
		d.queue.AddObject(gw)
	}
}