result = op->getResult(0);
    }
    rewriter.replaceOp(op, {result});
    return success();
  }
};

// Lowers ResizeNearestNeighbor to an indices computations with a gather along
// the combined spatial dimensions. Generating the indices along the
// width/height index could be used to gather along each of W and H dimension

// xla_cluster_name: XLA cluster name for this XLA computation. We need it
//   because XLA cluster name might be different from `func_name`.
// func_name_attrs: they will be used to instantiate the XLA computation func.
// new_func_name: new function name for rewritten XLA computation func.
// host_compute_core: mapping from outside compilation cluster name to XLA
//   device assignment.
// fld: FunctionLibraryDefinition object.

}

void init() {
        SansTypeface = loadfont();
}

// issue 5242
// Cgo incorrectly computed the alignment of structs
// with no Go accessible fields as 0, and then panicked on
// modulo-by-zero computations.

// issue 50987
// disable arm64 GCC warnings
#cgo CFLAGS: -Wno-psabi -Wno-unknown-warning-option

typedef struct {
} foo;

typedef struct {
	int x : 1;
} bar;

limitations under the License.
==============================================================================*/

// This file contains some utility functions for encapsulating XLA computation
// in host graph and encapsulating outside compilation in XLA computation.

#ifndef TENSORFLOW_COMPILER_JIT_ENCAPSULATE_UTIL_H_
#define TENSORFLOW_COMPILER_JIT_ENCAPSULATE_UTIL_H_

#include "absl/container/flat_hash_map.h"

.concat([fragments[divider].split(splitRegex)[0]]),\n        [fragments[divider].split(splitRegex)[1]].concat(\n          fragments.slice(divider + 1)\n        ),\n      ]\n    : [fragments];\n\n  // Convert the values with units to absolute pixels to allow our computations\n  ops = ops.map((op, index) => {\n    // Most of the units rely on the orientation of the popper\n    const measurement = (index === 1 ? !useHeight : useHeight)\n      ? 'height'\n      : 'width';\n    let mergeWithPrevious = false;\n    return...

    output_dims.insert(output_dims.begin() + axis, depth);

    Location loc = op.getLoc();

    // The iota result is the effective output shape of the computation,
    // and indices must be broadcast into it. At this point, this computation
    // would need to be reworked quite a bit to support dynamic shapes, so
    // just using static broadcasting.
    auto index_type =

//
// then pulling `f` out of the cluster may reduce the number of compilations and
// will never increase the number of compilations.
//
// We may reduce the number of compilations if f is many to one.  For instance
// if f(x,y) = x-y then x=3,y=1 and x=4,y=2 will generate two different
// compilations if f is in the cluster but only one compilation if f is outside
// the cluster.
//

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility...

// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));
  }
}

// Trivial test that runs the generated function to ensure it doesn't crash.
TEST(TEST_NAME, NoCrash) {

        private Callable<T> computation;
        private Try<T> result;

        public Deferred(Callable<T> computation) {
            this.computation = computation;
        }

        @Override
        public T get() {
            return result().get();
        }

        private Try<T> result() {
            if (result == null) {