// will make any future Write calls return errClosed
	err error
	// table is the hash table from 20-bit keys to 12-bit values. Each table
	// entry contains key<<12|val and collisions resolve by linear probing.
	// The keys consist of a 12-bit code prefix and an 8-bit byte suffix.
	// The values are a 12-bit code.
	table [tableSize]uint32
}

// writeLSB writes the code c for "Least Significant Bits first" data.

      return WalkResult::advance();
    OpBuilder builder(reduce_dataset);
    Location loc = reduce_dataset.getLoc();

    // Get reduce function signature for dataset iteration types.
    // Note: lookupSymbol is a linear lookup which means the overall
    // complexity = # ReduceDataset ops x # of functions in module.
    func::FuncOp reduce_func =
        function->getParentOfType<ModuleOp>().lookupSymbol<FuncOp>(

	unlock(&ticks.lock)
}

// minTimeForTicksPerSecond is the minimum elapsed time we require to consider our ticksPerSecond
// measurement to be of decent enough quality for profiling.
//
// There's a linear relationship here between minimum time and error from the true value.
// The error from the true ticks-per-second in a linux/amd64 VM seems to be:
// -   1 ms -> ~0.02% error
// -   5 ms -> ~0.004% error

	// We think of the mutator utilization over time as the
	// box-filtered utilization function, which we call the
	// "windowed mutator utilization function". The resulting
	// function is continuous and piecewise linear (unless
	// window==0, which we handle elsewhere), where the boundaries
	// between segments occur when either edge of the window
	// encounters a change in the instantaneous mutator

  }];

  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

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,