"editorMode": "code",
          "exemplar": true,
          "expr": "sum by (bucket,range) (minio_bucket_objects_size_distribution{job=\"$scrape_jobs\"})",
          "format": "time_series",
          "instant": false,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{bucket,range}}",
          "refId": "A",
          "step": 300
        }
      ],

nt":"bottom","showLegend":true},"tooltip":{"mode":"multi","sort":"none"}},"pluginVersion":"10.1.5","targets":[{"datasource":{"type":"prometheus","uid":"${datasource}"},"expr":"process_virtual_memory_bytes{app=\"istiod\"}","format":"time_series","instant":false,"intervalFactor":2,"legendFormat":"Virtual Memory","refId":"I","step":2},{"datasource":{"type":"prometheus","uid":"${datasource}"},"expr":"process_resident_memory_bytes{app=\"istiod\"}","format":"time_series","intervalFactor":2,"legendFormat":"Resident...

"percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "process_virtual_memory_bytes{app=\"istiod\"}", "format": "time_series", "instant": false, "intervalFactor": 2, "legendFormat": "Virtual Memory", "refId": "I", "step": 2 }, { "expr": "process_resident_memory_bytes{app=\"istiod\"}", "format": "time_series", "intervalFactor": 2, "legendFormat": "Resident Memory", "refId": "H", "step":...

          <type>String</type>
        </field>
        <field>
          <name>properties</name>
          <version>3.0.0+</version>
          <description>Properties about the contributor, such as an instant messenger handle.</description>
          <type>Properties</type>
          <association xml.mapStyle="inline">
            <type>String</type>
            <multiplicity>*</multiplicity>
          </association>

without actual Motion, it would not be able to agitate and heat the Bodies which refract and reflect it. If it consisted in Motion propagated to all distances in an instant, it would require an infinite force every moment, in every shining Particle, to generate that Motion. And if it consisted in Pression or Motion, propagated either in an instant or in time, it would bend into the Shadow. For Pression or Motion cannot be propagated in a Fluid in right Lines, beyond an Obstacle which stops part of the Motion,...

// CHECK-DAG:       [[VAL_92:%.*]] = arith.constant dense<0.000000e+00> : tensor<3xf32>
// CHECK-DAG:       [[VAL_93:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x3xf32>
// CHECK-DAG:       [[VAL_94:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x1xf32>
// CHECK-DAG:       [[VAL_95:%.*]] = arith.constant dense<0> : tensor<1xi64>
// CHECK-DAG:       [[VAL_96:%.*]] = arith.constant dense<1> : tensor<1xi64>

// CHECK-DAG:       [[VAL_8:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x16xf32>
// CHECK-DAG:       [[VAL_9:%.*]] = arith.constant dense<0.000000e+00> : tensor<16x8xf32>
// CHECK-DAG:       [[VAL_10:%.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG:       [[VAL_11:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x16xf32>
// CHECK-DAG:       [[VAL_12:%.*]] = arith.constant dense<0.000000e+00> : tensor<8x16xf32>

// CHECK-DAG: %[[BROADCAST_DIM:.+]] = arith.constant dense<{{\[3, 2, 1, 1, 4\]}}> : tensor<5xi32>
// CHECK-DAG: %[[EXPAND_DIM1:.+]] = arith.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[EXPAND_DIM0:.+]] = arith.constant dense<2> : tensor<1xi32>
// CHECK-DAG: %[[PERM:.+]] = arith.constant dense<{{\[1, 0, 2\]}}> : tensor<3xi32>

  }
};

// If the operand to a broadcastable op is a splat constant, try to replace it
// with a 0-d constant, e.g. before this optimization,
//   %cst = arith.constant dense<1.0> : tensor<16x16x4xf32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<16x16x4xf32>)
// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...

  %size_splits_0 = arith.constant dense<[8, 8]> : tensor<2xi32>
  %split_dim_0 = arith.constant dense<0> : tensor<i32>
  %0, %1 = "tfl.split_v"(%arg0, %size_splits_0, %split_dim_0) {num_splits = 2 : i32} : (tensor<16x4xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<8x4xf32>, tensor<8x4xf32>)

  %size_splits_1 = arith.constant dense<[2, 2]> : tensor<2xi32>
  %split_dim_1 = arith.constant dense<1> : tensor<i32>