}];

  let description = [{
That is for rows we have grad for, we update var, accum and linear as follows:
accum_new = accum + grad * grad
linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
accum = accum_new
  }];

  let arguments = (ins

	//   consisting entirely of two hyphen characters ("-",
	//   decimal value 45) followed by the boundary parameter
	//   value from the Content-Type header field, optional linear
	//   whitespace, and a terminating CRLF.
	if !bytes.HasPrefix(line, r.dashBoundary) {
		return false
	}
	rest := line[len(r.dashBoundary):]
	rest = skipLWSPChar(rest)

e:i})=>i("colors"),backgroundImage:{none:"none","gradient-to-t":"linear-gradient(to top, var(--tw-gradient-stops))","gradient-to-tr":"linear-gradient(to top right, var(--tw-gradient-stops))","gradient-to-r":"linear-gradient(to right, var(--tw-gradient-stops))","gradient-to-br":"linear-gradient(to bottom right, var(--tw-gradient-stops))","gradient-to-b":"linear-gradient(to bottom, var(--tw-gradient-stops))","gradient-to-bl":"linear-gradient(to bottom left, var(--tw-gradient-stops))","gradient-to-...

  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor. Always returns at least setSize + 2.
   */
  @VisibleForTesting

		arch:  arch,
		cconv: cconv,
	})
}

type regSet struct {
	lo    int
	hi    int
	Rconv func(int) string
}

// Few enough architectures that a linear scan is fastest.
// Not even worth sorting.
var regSpace []regSet

/*
	Each architecture defines a register space as a unique
	integer range.

}

LogicalResult ClusteringState::VerifyDominanceProperty(
    unsigned src_root, unsigned dst_root, Operation *insertion_point) {
  // TODO(ezhulenev): Optimize this linear scan with a map lookup.
  for (auto &member : members) {
    unsigned root = FindRoot(member.root);
    if (root != src_root) continue;

    // Block arguments do not really participate in clustering, they are only

def TF_XlaSparseCoreFtrlOp : TF_Op<"XlaSparseCoreFtrl", []> {
  let summary = "aaa";

  let arguments = (ins
    TF_Float32Tensor:$embedding_table,
    TF_Float32Tensor:$accumulator,
    TF_Float32Tensor:$linear,
    TF_Float32Tensor:$learning_rate,
    TF_Int32Tensor:$indices,
    TF_Float32Tensor:$gradient,
    TF_Float32Tensor:$beta,
    TF_Float32Tensor:$learning_rate_power,

	// Try to find the g that needs to be notified.
	// If it hasn't made it to the list yet we won't find it,
	// but it won't park itself once it sees the new notify number.
	//
	// This scan looks linear but essentially always stops quickly.
	// Because g's queue separately from taking numbers,
	// there may be minor reorderings in the list, but we
	// expect the g we're looking for to be near the front.

				j = h // preserves f(j) == true
			}
		}
		// i == j, f(i-1) == false, and f(j) (= f(i)) == true  =>  answer is i.

		// Having found the first, linear scan forward to find the last.
		// We could do a second binary search, but the caller is going
		// to do a linear scan anyway.
		for j := i; j < len(offs); j++ {
			typ := rtypeOff(section, offs[j])
			if stringFor(typ) != s {
				break
			}

.admonitionblock td.content {
	word-wrap: anywhere;
	background: var(--admonition-background);
	padding: 1rem 1rem 1rem 1rem;
	width: 100%;
	border-radius: 4px;
}

.admonitionblock td.icon {
	background: linear-gradient(90deg, rgba(0, 0, 0, .2) 0, rgba(0, 0, 0, .2)) no-repeat 0 /2.075em 100%;
	border-radius: .5em;
	font-size: calc(15/var(--rem-base)*1rem);
	left: 0;
	line-height: 1;
	padding: .25em .075em;