mutex_lock l(graph->mu);
  tensorflow::shape_inference::InferenceContext* ic =
      graph->refiner.GetContext(node);
  if (ic == nullptr) {
    status->status =
        InvalidArgument("Node ", node->name(), " was not found in the graph");
    return;
  }

  auto shape_and_type_vec =
      std::vector<tensorflow::shape_inference::ShapeAndType>(
          num_shapes_and_types);
  for (int i = 0; i < num_shapes_and_types; ++i) {

    return false;
  }
  bool found_dtype = false;
  bool found_shape = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {
      if (attr.second.type() == tensorflow::DT_INT32) {
        found_dtype = true;
      } else {
        return false;
      }
    } else if (attr.first == "shape") {
      found_shape = true;
    }
  }

}

TEST(TensorPjRtBufferUtilTest, GetPjRtCApiClientNotFound) {
  EXPECT_THAT(
      GetPjRtCApiClient(tensorflow::DeviceType(DEVICE_CPU)),
      StatusIs(error::NOT_FOUND,
               HasSubstr(absl::StrCat("PjRt client not found for device type ",
                                      DEVICE_CPU))));
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCApiClientIncorrectType) {
  TF_ASSERT_OK_AND_ASSIGN(

        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",
                                     dim_sizes[i],
                                     " found: ", shape.dim_size(i));
    }
  }

    TF_SetStatusFromGCSStatus(gcs_status, status);
    auto status_code = TF_GetCode(status);
    if (status_code != TF_OK && status_code != TF_NOT_FOUND) return;
    // If this file does not exist on server, we will need to sync it.
    bool sync_need = (status_code == TF_NOT_FOUND);
    file->plugin_file = new tf_writable_file::GCSFile(
        {std::move(bucket), std::move(object), &gcs_file->gcs_client,

    TF_RETURN_IF_ERROR(operation->Execute(
        absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));

    if (num_outputs != 1) {
      return errors::Internal("Expected 1 output but found ", num_outputs);
    }
    auto* t = dyn_cast<GraphTensor>(outputs[0]);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }

    CHECK_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (dev_type == device_type) {
      *device_name = dev_name;
      LOG(INFO) << "Found " << device_type << " device " << *device_name;
      TF_DeleteDeviceList(devices);
      return true;
    }
  }
  TF_DeleteDeviceList(devices);
  return false;
}

  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::NOT_FOUND);
}

TEST_P(ModularFileSystemTest, TestReadFileNonExisting) {
  const std::string filepath = GetURIForPath("dir_not_found/a_file");
  std::unique_ptr<RandomAccessFile> new_file;
  Status status = env_->NewRandomAccessFile(filepath, &new_file);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::NOT_FOUND);
}

  // Validate GraphDef is what we expect.
  bool found_placeholder = false;
  bool found_scalar_const = false;
  bool found_add = false;
  for (const auto& n : graph_def.node()) {
    if (IsPlaceholder(n)) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (IsScalarConst(n, 3)) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (IsAddN(n, 2)) {

// Copyright 2013 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// This .cc file will be automatically compiled by the go tool and
// included in the package.

#include <string>
#include "main.h"

std::string Caller::call() {
	if (callback_ != 0)
		return callback_->run();
	return "";