DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);
}

if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated...

        InvalidArgument("Node ", node->name(), " was not found in the graph");
    return -1;
  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  // Unknown rank means the number of dimensions is -1.
  if (!ic->RankKnown(shape)) {
    return -1;
  }

  return ic->Rank(shape);
}

void TF_GraphGetTensorShape(TF_Graph* graph, TF_Output output, int64_t* dims,

K-PKE.KeyGen according to FIPS 203, Algorithm 13. The two are merged to save // copies and allocations. func kemKeyGen1024(dk *DecapsulationKey1024, d, z *[32]byte) { dk.d = *d dk.z = *z g := sha3.New512() g.Write(d[:]) g.Write([]byte{k1024}) // Module dimension as a domain separator. G := g.Sum(make([]byte, 0, 64)) ρ, σ := G[:32], G[32:] dk.ρ = [32]byte(ρ) A := &dk.a for i := byte(0); i < k1024; i++ { for j := byte(0); j < k1024; j++ { A[i*k1024+j] = sampleNTT(ρ, j, i) } } var N byte s := &dk.s for i :=...

K-PKE.KeyGen according to FIPS 203, Algorithm 13. The two are merged to save // copies and allocations. func kemKeyGen1024(dk *DecapsulationKey1024, d, z *[32]byte) { dk.d = *d dk.z = *z g := sha3.New512() g.Write(d[:]) g.Write([]byte{k1024}) // Module dimension as a domain separator. G := g.Sum(make([]byte, 0, 64)) ρ, σ := G[:32], G[32:] dk.ρ = [32]byte(ρ) A := &dk.a for i := byte(0); i < k1024; i++ { for j := byte(0); j < k1024; j++ { A[i*k1024+j] = sampleNTT(ρ, j, i) } } var N byte s := &dk.s for i :=...

The elements can be addressed by integer <a href="#Index_expressions">indices</a>
0 through <code>len(a)-1</code>.
Array types are always one-dimensional but may be composed to form
multi-dimensional types.
</p>

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

<p>

minimum font size\n      $fs-diff: $fs - $fs-min;\n\n      // Base font-size formatting\n      $min-width: if($rfs-font-size-unit == rem, #{divide($fs-min, $rfs-rem-value)}rem, #{$fs-min}px);\n\n      // Use `vmin` if two-dimensional is enabled\n      $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n      // Calculate the variable width between 0 and $rfs-breakpoint\n      $variable-width: #{divide($fs-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n      // Set the calculated font-size\n     ...

* Fix for Pod stuck in ContainerCreating with error "Volume is not yet attached according to node". ([#50806](https://github.com/kubernetes/kubernetes/pull/50806), [@verult](https://github.com/verult))
* Fix initial exec terminal dimensions. ([#51126](https://github.com/kubernetes/kubernetes/pull/51126), [@chen-anders](https://github.com/chen-anders))

* Fixed regression in initial kubectl exec terminal dimensions ([#51127](https://github.com/kubernetes/kubernetes/pull/51127), [@chen-anders](https://github.com/chen-anders))

- Kubernetes now includes a metric that reports the number of PVCs that are in-use,with plugin and node name as dimensions, making it possible to figure out how many PVCs each node is using when troubleshooting attach/detach issues.
 ([#64527](https://github.com/kubernetes/kubernetes/pull/64527), [@gnufied](https://github.com/gnufied))