#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
namespace {

// TODO(skyewm): make nodes in while loop unfetchable like in Python version
void TF_FinishWhileHelper(const TF_WhileParams* params, TF_Status* status,
                          TF_Output* outputs) {
  if (!ValidateInputWhileParams(*params, status)) return;

  AbstractContext* ctx_;
};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {
  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never

  // Requests that the worker thread execute the specified operation. Blocks
  // until the previously pending operation (a StartExecute without a Join) has
  // finished, if any.
  //
  // `cancellation_manager` must live until after `Join` finishes and pending
  // `is_async` operations finish. In addition to allowing the caller to cancel
  // the operation, its `StartCancel` method will be called if op execution

    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
    }

    TF_DeleteTensor(dst);
  }
}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});
  TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,

#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/stacktrace_handler.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/command_line_flags.h"

#if defined(PLATFORM_WINDOWS)
// Make mkdir resolve to _mkdir to create the test temporary directory.
#include <direct.h>
#define mkdir(name, mode) _mkdir(name)

        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,

// We will work with content_view instead of content.
static const absl::string_view content_view = content;

namespace gcs = google::cloud::storage;

static std::string InitializeTmpDir() {
  // This env should be something like `gs://bucket/path`
  const char* test_dir = getenv("GCS_TEST_TMPDIR");
  if (test_dir != nullptr) {
    std::string bucket, object;
    TF_Status* status = TF_NewStatus();

      "TFE_ContextSetServerDef not supported on mobile");
  return false;
#else   // !defined(IS_MOBILE_PLATFORM)
  bool is_alive;
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->CheckRemoteAlive(
          worker_name, &is_alive);
  return is_alive;
#endif  // !IS_MOBILE_PLATFORM
}

TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,

  }

  // Define function, use it in graph, and run
  DefineT(-1, {}, {{feed1, 0}, {feed2, 0}}, {outputs[0]}, {});
  TF_Operation* five = ScalarConst(5, host_graph_, s_, "five");
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed, five});
  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.

  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,