1 row in set (0.01 sec)

```

## Publish MinIO events via Kafka

Install Apache Kafka from [here](http://kafka.apache.org/).

### Step 1: Ensure minimum requirements are met

tag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+zt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Vt(_t,v,t,T)){if(T.readyState=1,g&&m.trigger(...

15% {\n    -webkit-transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg);\n    transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg);\n  }\n  30% {\n    -webkit-transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg);\n    transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg);\n  }\n  45% {\n    -webkit-transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg);\n    transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg);\n  }\n  60% {\n    -webkit-transform: translate3d(10%,...

tag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+zt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Vt(_t,v,t,T)){if(T.readyState=1,g&&m.trigger(...

tag[d])),(k.data&&k.hasContent&&k.contentType!==!1||b.contentType)&&v.setRequestHeader("Content-Type",k.contentType),v.setRequestHeader("Accept",k.dataTypes[0]&&k.accepts[k.dataTypes[0]]?k.accepts[k.dataTypes[0]]+("*"!==k.dataTypes[0]?", "+pc+"; q=0.01":""):k.accepts["*"]);for(j in k.headers)v.setRequestHeader(j,k.headers[j]);if(k.beforeSend&&(k.beforeSend.call(l,v,k)===!1||2===t))return v.abort();u="abort";for(j in{success:1,error:1,complete:1})v[j](k[j]);if(c=sc(oc,k,b,v)){v.readyState=1,i&&m....

    </magic>
  </mime-type>
  <mime-type type="application/x-netcdf">
    <glob pattern="*.nc"/>
    <glob pattern="*.cdf"/>
    <magic priority="50">
      <match value="CDF\001" type="string" offset="0" />
      <match value="CDF\002" type="string" offset="0" />
      <match value="0x43444601" type="string" offset="0"/>
    </magic>
  </mime-type>
  <mime-type type="application/x-parquet">

$carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  @if $enable-gradients {\n    background-image: linear-gradient(90deg, rgba($black, .25), rgba($black, .001));\n  }\n}\n.carousel-control-next {\n  right: 0;\n  @if $enable-gradients {\n    background-image: linear-gradient(270deg, rgba($black, .25), rgba($black, .001));\n  }\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height:...

$carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  @if $enable-gradients {\n    background-image: linear-gradient(90deg, rgba($black, .25), rgba($black, .001));\n  }\n}\n.carousel-control-next {\n  right: 0;\n  @if $enable-gradients {\n    background-image: linear-gradient(270deg, rgba($black, .25), rgba($black, .001));\n  }\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height:...

187F8..187FF  ; disallowed                             # NA   <reserved-187F8>..<reserved-187FF>
18800..18AF2  ; valid                                  # 9.0  TANGUT COMPONENT-001..TANGUT COMPONENT-755
18AF3..18CD5  ; valid                                  # 13.0 TANGUT COMPONENT-756..KHITAN SMALL SCRIPT CHARACTER-18CD5

    add explicit dependencies on :pooling_ops if they reference the operators
    from that library.
*   tf.keras.optimizers default learning rate changes:
    *   Adadelta: 1.000 to 0.001
    *   Adagrad: 0.01 to 0.001
    *   Adamax: 0.002 to 0.001
    *   NAdam: 0.002 to 0.001

## Bug Fixes and Other Changes

*   Documentation
*   Deprecations and Symbol renames.