#include "tensorflow/compiler/mlir/tf2xla/internal/logging_hooks.h"
#include "tensorflow/core/framework/metrics.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/stacktrace.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/tpu/tpu_defs.h"
#include "tensorflow/core/util/debug_data_dumper.h"
#include "tsl/framework/device_type.h"
#include "tsl/platform/error_logging.h"

    case it is mutable).

    The `type` attribute contains the tensor's type, which for the case of
    mutable tensors might be more general than just the fixed static shape of
    the `value` attribute. For example, a global tensor might be unranked such
    as `tensor<*xf32>`, or a more complex shape such as `tensor<4x?x27xf32>`.
    The shape of `value` must be compatible with the shape of `type` in the

#include <memory>
#include <string>

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tensorflow/core/util/tensor_slice_reader.h"

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

system-installed Python packages. \
Instead, an independent Python toolchain is registered, ensuring the right
dependencies are always used. \
See https://github.com/bazelbuild/rules_python/ for more details.

### Specifying the Python version

Note: Only a number of minor Python versions are supported at any given time.

By default, the lowest supported version is used.

    if (!expand_op.getResult().hasOneUse()) {
      return rewriter.notifyMatchFailure(
          expand_op, "result for current op has more than 1 use");
    }
    if (!squeeze_op.getResult().hasOneUse()) {
      return rewriter.notifyMatchFailure(
          squeeze_op, "result for current op has more than 1 use");
    }
    // Make sure that the axis in `expand_op` is constant.
    if (auto const_op =

#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/process_function_library_runtime.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/function.pb.h"

// RUN: tf-opt -verify-diagnostics -allow-unregistered-dialect \
// RUN:        -split-input-file -tf-resource-analyzer-test %s \
// RUN:   | FileCheck %s

// TODO(b/269548549): Add tests to cover more patterns.

// Test that VarHandleOp is not marked as "potentially written" if it is not
// assigned inside the function called by "tf.BatchFunction".

module {
// CHECK-LABEL: @serving_default
  func.func @serving_default() -> (tensor<*xi32>) {

                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,
                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

void AddPostQuantizationStableHloToTfPasses(
    const mlir::TFL::PassConfig& pass_config,
    mlir::OpPassManager& pass_manager);

// This is the early part of the conversion in isolation. This enables a caller
// to inject more information in the middle of the conversion before resuming it
// (like freezing variables for example).
void AddPreVariableFreezingTFToTFLConversionPasses(
    const mlir::TFL::PassConfig& pass_config,

#include "absl/strings/string_view.h"
#include "json/config.h"
#include "json/json.h"
#include "json/writer.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/gauge.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/protobuf/fingerprint.pb.h"

namespace tensorflow {
namespace metrics {

namespace {