t: t,
		objAPITest: func(obj ObjectLayer, instanceType, bucketName string, apiRouter http.Handler, credentials auth.Credentials, t *testing.T) {
			objectName := "dummy-object"
			functionID := "lambda1"
			functionToken := "token123"

			// Lambda mock server
			lambdaServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
				w.Header().Set(xhttp.AmzRequestRoute, functionID)

		Description:    desc,
		Code:           code,
	}
}

// GetObjectLambdaHandler - GET Object with transformed data via lambda functions
// ----------
// This implementation of the GET operation applies lambda functions and returns the
// response generated via the lambda functions. To use this API, you must have READ access
// to the object.

## Example Lambda handler

Install the necessary dependencies.
```sh
pip install flask requests
```

Following is an example lambda handler.
```py
from flask import Flask, request, abort, make_response
import requests

app = Flask(__name__)

		return ""
	}

	return "arn:minio:s3-object-lambda:" + arn.region + ":" + arn.TargetID.String()
}

// ParseARN - parses string to ARN.
func ParseARN(s string) (*ARN, error) {
	// ARN must be in the format of arn:minio:s3-object-lambda:<REGION>:<ID>:<TYPE>
	if !strings.HasPrefix(s, "arn:minio:s3-object-lambda:") {
		return nil, &ErrInvalidARN{s}
	}

	tokens := strings.Split(s, ":")

     * to avoid the extra class for the lambda (and maybe more for memoizingSupplier itself) and the
     * indirection.
     *
     * One thing to *avoid* is a change to make each Logger user use memoizingSupplier directly:
     * That may introduce an extra class for each lambda (currently a dozen).
     */
    Logger local = logger;
    if (local != null) {
      return local;

        def __init__(self):
            self.data = [1.0, 2.0, 3.0]

    FakeNumpyArrayPydantic = Annotated[
        FakeNumpyArray,
        WithJsonSchema(TypeAdapter(list[float]).json_schema()),
        PlainSerializer(lambda v: v.data),
    ]

    class MyModel(BaseModel):
        model_config = ConfigDict(arbitrary_types_allowed=True)
        custom_field: FakeNumpyArrayPydantic

    app = FastAPI()

    @app.get("/")

		configLogIf(ctx, fmt.Errorf("Unable to initialize notification target(s): %w", err))
	}

	bootstrapTraceMsg("initialize the lambda targets")
	globalLambdaTargetList, err = lambda.FetchEnabledTargets(GlobalContext, s, transport)
	if err != nil {
		configLogIf(ctx, fmt.Errorf("Unable to initialize lambda target(s): %w", err))
	}

	bootstrapTraceMsg("applying the dynamic configuration")
	// Apply dynamic config values

  /**
   * Starts specifying how to combine two {@link ClosingFuture}s into a single pipeline, assuming
   * they all succeed. If any fail, the resulting pipeline will fail.
   *
   * <p>Calling this method allows you to use lambdas or method references typed with the types of
   * the input {@link ClosingFuture}s.
   *
   * @throws IllegalStateException if a {@code ClosingFuture} has already been derived from any of

     * This method looks up the user by their code and creates a new favorite log entry
     * using the provided lambda function to populate the favorite log data.
     *
     * @param userCode the unique code identifying the user
     * @param favoriteLogLambda a lambda function that accepts UserInfo and FavoriteLog to populate the favorite log data

 * someGraphAlgorithm(startNode, MyNode::getParents);
 * }
 *
 * <p>If you have some other mechanism for returning the predecessors of a node, or one that doesn't
 * return a {@code Iterable<? extends N>}, then you can use a lambda to perform a more general
 * transformation:
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, node -> ImmutableList.of(node.mother(), node.father()));
 * }
 *