const char** containers, int ncontainers,
                            TF_Status* status) {
  std::vector<string> container_names(ncontainers);
  for (int i = 0; i < ncontainers; ++i) {
    container_names[i] = containers[i];
  }

  status->status = Reset(opt->options, container_names);
}

extern "C" {

void TF_Reset(const TF_SessionOptions* opt, const char** containers,

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

#include "tensorflow/c/eager/c_api_unified_experimental.h"

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"

             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and
// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(

  auto cc_status =
      cc_ctx->resource_manager()->Create<tensorflow::PluginResource>(
          container_name, plugin_resource_name, cc_resource_ptr);
  status->status = cc_status;
}

void TF_LookupOrCreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void** result_plugin_resource,
    void* (*create_func)(void*), void* create_func_args,

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradients.h"

#include <memory>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_experimental.h"

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,

                         std::vector<std::string>* control_ret_node_names,
                         bool* control_rets_updated) override {
    // Inject failure to function instantiation if finding a node that contains
    // the given node name (error_node_) and requested device (error_device_).
    for (const auto node : graph->get()->nodes()) {
      if (node->name().find(error_node_) != string::npos &&

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,

/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,

        absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
        &retvals));
    int skipped_indices = 0;
    for (int i = 0; i < outputs.size(); i++) {
      if (!null_indices.contains(i)) {
        outputs[i] = fn_outputs[i - skipped_indices];
      } else {
        skipped_indices += 1;
      }
    }
    TF_RETURN_IF_ERROR(ctx->RemoveFunction(fn_name));
    return absl::OkStatus();