// (1) The base case for the analysis is that if the loop body does not execute
//     at all, the resource IDs for each result is the same as the resource IDs
//     of the corresponding input.
// (2) If the loop does execute one or more times, then we need to account for
//     data flow through the body of the while loop. If result #r is the same
//     as arg #a of the loop body (pass through argument), then we can reason

// executable.
//
// Returns a non-OK Status if no unambiguous choice of device exists.
//
// We choose the device using the following rules:
//
//  - It is an error for `device_names` to contain more than one device of the
//    same type.
//  - GPU is preferred over CPU.
//  - If `allow_mixing_unknown_and_cpu` is true then unknown devices are
//    preferred over CPU.

//   FusedLoc[NameLoc(op_type:), ..., NameLoc(node_name@func_name)] or
//   FusedLoc[NameLoc(op_type:), ..., CallSiteLoc(node_name@func_name)]. See
// tensorflow/compiler/mlir/tensorflow/translate/import_model.cc for more
// details.
bool IsImportLocPattern(FusedLoc loc) {
  ArrayRef<Location> locations = mlir::cast<FusedLoc>(loc).getLocations();
  if (locations.size() < 2 || !isa<NameLoc>(locations.front())) return false;

#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/platform/env.h"

  return broadcasted_type != Type() && broadcasted_type == expected_output;
}

// Returns whether if `type1` dimensions are the same as the ending dimensions
// of `type2`. This is more restricted than broadcastable.
bool IsTailOfShape(Type type1, Type type2) {
  auto tail_type = mlir::dyn_cast<ShapedType>(type1);
  auto full_type = mlir::dyn_cast<ShapedType>(type2);

flow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/test...

#include "tensorflow/compiler/mlir/tensorflow/utils/side_effect_analysis_util.h"
#include "tensorflow/core/framework/kernel_shape_util.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"

namespace mlir {
namespace TF {

namespace {

#include "xla/status_macros.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/profile_utils/cpu_utils.h"

      rewriter.replaceOp(identity, identity.getInput());
      return success();
    }
    // Replace the op with the input if output is only used by TF ops.
    // Currently this is more on the conservative side since we need to ensure
    // every consumer op to be a TF op before applying this pattern. We can
    // consider to revisit this in the future if this turns out to be too
    // restrictive.

// converting them to NHWC formats along with extra optimizations such as
// constant folding the transpose->convolution pattern. This is useful when
// downstream pipeline (e.g. XLA) is more optimized when accepting NHWC formats.
void AddProcessNchwTensorPasses(OpPassManager& pm);

// Registers quantization pass pipelines. This is only required when running