// END
            }});

            // GENERATE public Object invokeMethod(String name, Object params) { return getAsDynamicObject().invokeMethod(name, (Object[])params); }
            addGetter("invokeMethod", OBJECT_TYPE, RETURN_OBJECT_FROM_STRING_OBJECT, getter -> new MethodVisitorScope(getter) {{

				MatchConditions: []admissionregistration.MatchCondition{{
					Name:       "hasParams",
					Expression: `params.foo == "okay"`,
				}},
				Validations: []admissionregistration.Validation{{
					Expression: "object.x < 100",
				}},
			},
		},
		expectedError: `undeclared reference to 'params'`,
	}, {
		name: "variable composition empty name",
		config: &admissionregistration.ValidatingAdmissionPolicy{

from fastapi.logger import logger
from fastapi.openapi.docs import (
    get_redoc_html,
    get_swagger_ui_html,
    get_swagger_ui_oauth2_redirect_html,
)
from fastapi.openapi.utils import get_openapi
from fastapi.params import Depends
from fastapi.types import DecoratedCallable, IncEx
from fastapi.utils import generate_unique_id
from starlette.applications import Starlette
from starlette.datastructures import State

    TFL_TensorOf<[I16, I32, I64]>:$indices,
    I32Attr:$axis,
    DefaultValuedOptionalAttr<I32Attr, "0">:$batch_dims
  );

  let builders =
  [
    OpBuilder<(ins "Value":$params, "Value":$indices, "IntegerAttr":$axis, "IntegerAttr":$batch_dims),
    [{ BuildGatherOp(&$_builder, $_state, params, indices, axis, batch_dims); }]>
  ];

  let results = (outs

  ...

			Algorithm:  hashOID,
			Parameters: asn1.NullRawValue,
		}

		var err error
		params.MGF.Parameters.FullBytes, err = asn1.Marshal(mgf1Params)
		if err != nil {
			t.Fatalf("failed to marshal MGF parameters: %s", err)
		}

		serialized, err := asn1.Marshal(params)
		if err != nil {
			t.Fatalf("failed to marshal parameters: %s", err)
		}

		return serialized
	}

  return {};
}

ValuePort ComputeInputComponentFor(GatherV2Op op, ArrayRef<unsigned int> port) {
  if (port.size() != 2) return {};
  assert(port[0] == 0);

  auto params = op.getParams();
  auto params_ty = mlir::dyn_cast<RankedTensorType>(params.getType());
  if (!params_ty || !params_ty.hasStaticShape() || params_ty.getRank() != 1 ||
      op.getBatchDims() != 0) {
    return {};
  }

  DenseIntElementsAttr axis;

  ...

  }
  return false;
}

class ConvertGatherOp : public OpConversionPattern<mhlo::GatherOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  // Helper params for representing the transpose params for the "canonicalized"
  // output to the real output.
  struct TransposeParams {
    std::vector<int64_t> permutation;
    // The following are the "canonicalized" output shape with offset dims.

      "properties": {
        "rollingUpdate": {
          "$ref": "#/definitions/io.k8s.api.apps.v1.RollingUpdateDeployment",
          "description": "Rolling update config params. Present only if DeploymentStrategyType = RollingUpdate."
        },
        "type": {
          "description": "Type of deployment. Can be \"Recreate\" or \"RollingUpdate\". Default is RollingUpdate.",