[32]byte
[2*N] struct { x, y int32 }
[1000]*float64
[3][5]int
[2][2][2]float64  // same as [2]([2]([2]float64))
</pre>

<h3 id="Slice_types">Slice types</h3>

<p>
A slice is a descriptor for a contiguous segment of an <i>underlying array</i> and
provides access to a numbered sequence of elements from that array.
A slice type denotes the set of all slices of arrays of its element type.

    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "continuous-GrYlRd"
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",

   * require a (different in each case) function that translates storedPermits to throttling time.
   *
   * This role is played by storedPermitsToWaitTime(double storedPermits, double permitsToTake). The
   * underlying model is a continuous function mapping storedPermits (from 0.0 to maxStoredPermits)
   * onto the 1/rate (i.e. intervals) that is effective at the given storedPermits. "storedPermits"

   *
   * @since 8.0
   */
  public static final byte SI = 15;

  /**
   * Data Link Escape: A communication control character which will change the meaning of a limited
   * number of contiguously following characters. It is used exclusively to provide supplementary
   * controls in data communication networks.
   *
   * @since 8.0
   */
  public static final byte DLE = 16;

  /**

	T7 [10]struct{ f []T7 }   // T7 contains T7 as component of a slice in a struct
)
</pre>

<h3 id="Slice_types">Slice types</h3>

<p>
A slice is a descriptor for a contiguous segment of an <i>underlying array</i> and
provides access to a numbered sequence of elements from that array.
A slice type denotes the set of all slices of arrays of its element type.

just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After

        `tf.config.experimental.enable_tensor_float_32_execution(False)`.
    *   The byte layout for string tensors across the C-API has been updated to
        match TF Core/C++; i.e., a contiguous array of
        `tensorflow::tstring`/`TF_TString`s.
    *   C-API functions `TF_StringDecode`, `TF_StringEncode`, and
        `TF_StringEncodedSize` are no longer relevant and have been removed; see

        The {@code <ciManagement>} element contains information required to the
        continuous integration system of the project.
        ]]>
      </description>
      <fields>
        <field>
          <name>system</name>
          <version>4.0.0+</version>
          <description>
            The name of the continuous integration system, e.g. {@code continuum}.
          </description>
          <type>String</type>

	defer func() {
		o.debugln(color.Green("saveMetaCacheStream:")+"err:", err)
		if err != nil && !errors.Is(err, io.EOF) {
			go mc.setErr(err.Error())
			cancel()
		}
	}()

	defer cancel()
	// Save continuous updates
	go func() {
		var err error
		ticker := time.NewTicker(10 * time.Second)
		defer ticker.Stop()
		var exit bool
		for !exit {
			select {
			case <-ticker.C:
			case <-ctx.Done():

# LINUX ARM64 PYCPP
# In Linux Arm64 presubmit/continuous build, we cross-compile the binaries on
# Linux x86 so that we can use RBE. Since tests still need to run on the single
# host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit.