}
        block->cond_var.SignalAll();
        return;
      case FetchState::FETCHING:
        block->cond_var.WaitWithTimeout(&block->mu, absl::Minutes(1));
        if (block->state == FetchState::FINISHED) {
          return TF_SetStatus(status, TF_OK, "");
        }
        // Re-loop in case of errors.
        break;
      case FetchState::FINISHED:
        return TF_SetStatus(status, TF_OK, "");
    }
  }

    calls++;
    char c = (filename == "a") ? 'a' : (filename == "b") ? 'b' : 'x';
    if (offset > 0) {
      // The first block is lower case and all subsequent blocks are upper case.
      c = toupper(c);
    }
    memset(buffer, c, n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  // This cache has space for 4 blocks; we'll read from two files.

    command_type = invocation_results.get('command_type')

    case_attrib = attrib.copy()
    if command_type:
      command_type = command_type.title()
      case_name = f'{command_type} invocation {invocation_id}'
    else:
      case_name = f' Invocation {invocation_id}'
    case_attrib.update({'name': case_name,
                        'status': 'run', 'result': 'completed'})

#include "tensorflow/core/protobuf/config.pb.h"

using tensorflow::string;

namespace tensorflow {
namespace {

static bool HasSubstr(absl::string_view base, absl::string_view substr) {
  bool ok = absl::StrContains(base, substr);
  EXPECT_TRUE(ok) << base << ", expected substring " << substr;
  return ok;
}

TEST(CAPI, MonitoringCounter0) {
  TF_Status* status = TF_NewStatus();
  auto* counter =

TF_CAPI_EXPORT extern void TFE_ContextEndStep(TFE_Context* ctx);

#ifdef __cplusplus
} /* end extern "C" */
#endif

#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()

-       case 9:								      \
-	 __u->__ui = __c * 0x01010101;					      \
-	 __u = __extension__ ((void *) __u + 4);			      \
-       case 5:								      \
-	 __u->__ui = __c * 0x01010101;					      \
-	 __u = __extension__ ((void *) __u + 4);			      \
-       case 1:								      \
-	 __u->__uc = (unsigned char) __c;				      \
-	 break;								      \
-									      \
-       case 16:								      \

  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:
    case TF_DataType::TF_DOUBLE:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:
    case TF_DataType::TF_INT64:

       ++device_index) {
    DeviceThread* device_thread = device_threads_[device_index].get();
    per_device_output_tensors.push_back(device_thread->Join(status));
    // We will run every Join even if there are bad statuses in case the user
    // wants to recover and continue running ops on the parallel device (which
    // would otherwise deadlock).
    if (TF_GetCode(status) != TF_OK &&
        (first_bad_status == nullptr

                       const char* dst, TF_Status* status) {
  // If target is a directory return TF_FAILED_PRECONDITION.
  // Target might be missing, so don't error in that case.
  struct stat st;
  if (stat(dst, &st) != 0) {
    if (errno != ENOENT) {
      TF_SetStatusFromIOError(status, errno, dst);
      return;
    }
  } else if (S_ISDIR(st.st_mode)) {