# A tape built on top of unified execution APIs.
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

cc_library(
    name = "tape_context",
    srcs = ["tape_context.cc"],
    hdrs = [
        "tape_context.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [

                           ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
Status Execute(AbstractOperation*, AbstractContext*,
               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation*, Tape*, const GradientRegistry&);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

}

type memoryBasicInformation struct {
	baseAddress       uintptr
	allocationBase    uintptr
	allocationProtect uint32
	regionSize        uintptr
	state             uint32
	protect           uint32
	type_             uint32
}

// https://learn.microsoft.com/en-us/windows-hardware/drivers/ddi/wdm/ns-wdm-_osversioninfow
type _OSVERSIONINFOW struct {
	osVersionInfoSize uint32
	majorVersion      uint32

    from fastapi._compat import is_pv1_scalar_field

    field_info = FieldInfo()
    field = ModelField(
        name="foo",
        field_info=field_info,
        type_=Union[str, List[int]],
        class_validators={},
        model_config=BaseConfig,
    )
    assert not is_pv1_scalar_field(field)


@needs_pydanticv2
def test_get_model_config():

==============================================================================*/
#include "tensorflow/c/eager/tracing_utils.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace tracing {

    labels = tf.one_hot(features['label'], num_classes)

    with tf.GradientTape() as tape:
      logits = model(inputs)
      loss_value = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels, logits))

    grads = tape.gradient(loss_value, model.trainable_variables)
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

                       TestPartResult::Type type,
                       const string& substr);
  ~SingleFailureChecker();
 private:
  const TestPartResultArray* const results_;
  const TestPartResult::Type type_;
  const string substr_;

  GTEST_DISALLOW_COPY_AND_ASSIGN_(SingleFailureChecker);
};

}  // namespace internal

}  // namespace testing

                       TestPartResult::Type type,
                       const string& substr);
  ~SingleFailureChecker();
 private:
  const TestPartResultArray* const results_;
  const TestPartResult::Type type_;
  const string substr_;

  GTEST_DISALLOW_COPY_AND_ASSIGN_(SingleFailureChecker);
};

}  // namespace internal

}  // namespace testing