tensors will be split into shards when the saver writes the checkpoint
      shards to disk. `tf.train.experimental.ShardByTaskPolicy` is the default
      sharding behavior, but `tf.train.experimental.MaxShardSizePolicy` can be
      used to shard the checkpoint with a maximum shard file size. Users with
      advanced use cases can also write their own custom

            "type": "string"
          },
          "kind": {
            "description": "kind expected values are Shared: multiple blob disks per storage account  Dedicated: single blob disk per storage account  Managed: azure managed data disk (only in managed availability set). defaults to shared",
            "type": "string"
          },
          "readOnly": {

						SecretName: "key",
						ShareName:  "share",
						ReadOnly:   false,
					},
				},
			},
		}, {
			name: "AzureFile empty secret",
			vol: core.Volume{
				Name: "azure-file",
				VolumeSource: core.VolumeSource{
					AzureFile: &core.AzureFileVolumeSource{
						SecretName: "",
						ShareName:  "share",
						ReadOnly:   false,
					},
				},
			},

    merged[indices[m][i], ...] = data[m][i, ...]
```

Each `data[i].shape` must start with the corresponding `indices[i].shape`,
and the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we
must have `data[i].shape = indices[i].shape + constant`.  In terms of this
`constant`, the output shape is

    merged.shape = [max(indices) + 1] + constant

            "type": "string"
          },
          "reservedFor": {

11180..111C4  ; valid                                  # 6.1  SHARADA SIGN CANDRABINDU..SHARADA OM
111C5..111C8  ; valid                  ;      ; NV8    # 6.1  SHARADA DANDA..SHARADA SEPARATOR
111C9..111CC  ; valid                                  # 8.0  SHARADA SANDHI MARK..SHARADA EXTRA SHORT VOWEL MARK
111CD         ; valid                  ;      ; NV8    # 7.0  SHARADA SUTRA MARK

                  "Distinct" Listeners have the following property:


                  The implementation can match inbound requests to a single distinct
                  Listener. When multiple Listeners share values for fields (for
                  example, two Listeners with the same Port value), the implementation
                  can match requests to only one of the Listeners using other
                  Listener fields.