return nil
			}
		}
		opts := updateServiceAccountOpts{
			secretKey:     change.Update.SecretKey,
			status:        change.Update.Status,
			name:          change.Update.Name,
			description:   change.Update.Description,
			sessionPolicy: sp,
			expiration:    change.Update.Expiration,
		}

			adminLogIf(ctx, fmt.Errorf("server update failed with %w", err))
			writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)
			return
		}
	}

	err = verifyBinary(u, sha256Sum, releaseInfo, mode, bytes.NewReader(bin))
	if err != nil {
		adminLogIf(ctx, fmt.Errorf("server update failed with %w", err))
		writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)

		}
		for _, rinfo := range rinfos.Targets {
			if rinfo.ReplicationStatus != rinfo.PrevReplicationStatus {
				rinfo.OpType = opType // update optype to reflect correct operation.
				globalReplicationStats.Load().Update(bucket, rinfo, rinfo.ReplicationStatus, rinfo.PrevReplicationStatus)
			}
		}
	}

	sendEvent(eventArgs{
		EventName:  eventName,
		BucketName: bucket,

			w.Header()[xhttp.AmzVersionID] = []string{gr.ObjInfo.VersionID}
			w.Header()[xhttp.AmzDeleteMarker] = []string{strconv.FormatBool(gr.ObjInfo.DeleteMarker)}
		}

		// Update context bucket & object names for correct S3 XML error response
		reqInfo := logger.GetReqInfo(ctx)
		reqInfo.BucketName = srcBucket
		reqInfo.ObjectName = srcObject

    status->status = tensorflow::errors::OutOfRange(
        "Cannot update edge. Input index [", dst.index,
        "] is greater than the number of total inputs [", ic_dst->num_inputs(),
        "].");
    return;
  }
  if (!ic_dst->MergeInput(dst.index, shape)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Cannot update edge, incompatible shapes: ", ic_dst->DebugString(shape),

    }

    public void setPomPath(java.nio.file.Path pomPath) {
        update( getDelegate().withPomFile(pomPath));
    }

    public void setModelEncoding(String modelEncoding) {
        update(getDelegate().with().modelEncoding(modelEncoding).build());
    }

    /**
     * Gets the base directory for the corresponding project (if any).
     *

            if annotation_is_pydantic_v1(self.response_model):
                raise PydanticV1NotSupportedError(
                    "pydantic.v1 models are no longer supported by FastAPI."
                    f" Please update the response model {self.response_model!r}."
                )
            self.response_field = create_model_field(
                name=response_name,
                type_=self.response_model,