#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"

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  EXPECT_EQ(string("fake"), string(src_names[0]));
  EXPECT_EQ(0, src_indexes[0]);

  TF_DeleteImportGraphDefResults(results);
  TF_DeleteImportGraphDefOptions(opts);
  TF_DeleteBuffer(graph_def);
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

TEST(CAPI, Session) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.

// ResourceMgr to clean up the resource. `status` will be set. If `status` is
// not OK, `*result_plugin_resource` will be set as nullptr.
//
// Caller does not take ownership of the `plugin_resource`.
TF_CAPI_EXPORT extern void TF_LookupOrCreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void** result_plugin_resource,

.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**
/tensorflow/lite/tools/make/gen/**
/api_init_files_list.txt
/estimator_api_init_files_list.txt
*.whl

# Android
.gradle
.idea
*.iml
local.properties
gradleBuild

# iOS
*.pbxproj
*.xcworkspace

# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

Status CreateParamsForInputs(AbstractContext* ctx,
                             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>,

    theta_inputs[input_index] = thetaMinus.get();
    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fMinus(f_outputs[0]);

    // Take Difference of both estimates: (f(theta + eps) - f(theta - eps)).
    TF_RETURN_IF_ERROR(
        ops::Sub(ctx, fPlus.get(), fMinus.get(), f_outputs, "sub_top"));
    AbstractTensorHandlePtr fDiff(f_outputs[0]);

    tensor_stack.pop_back();
    auto op_id_it = tensor_tape.find(tensor_id);
    if (op_id_it == tensor_tape.end()) {
      continue;
    }
    int64_t op_id = op_id_it->second;
    auto op_it = op_tape->find(op_id);
    auto result_op_it = result.op_tape.find(op_id);
    if (op_id == -1 || op_it == op_tape->end() ||
        result_op_it != result.op_tape.end()) {
      continue;
    }

                                const char* attr_name, const void* proto,
                                size_t proto_len, TF_Status* status) {
  // shape.ParseFromArray takes an int as length, this function takes size_t,
  // make sure there is no information loss.
  if (proto_len > std::numeric_limits<int>::max()) {
    status->status = InvalidArgument(
        "proto_len (", proto_len,