memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

// Return a scalar tensor handle with given values.
template <class T, TF_DataType datatype>

  /// and any buffers need to be allocated through filesystem allocation API.
  /// Filesystems may choose to ignore configuration errors but should at least
  /// display a warning or error message to warn the users.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if options are updated.
  ///   * Might use any other error value for `status` to signal other errors.
  ///

    cat $BATS_TEST_TMPDIR/actual_missing_licenses
  fi

  # Fail if either of the two "extras" or "missing" lists are present. If so,
  # then the user will see the above error messages.
  [[ ! -s $BATS_TEST_TMPDIR/actual_extra_licenses ]] && [[ ! -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]
}

@test "Pip package generated license includes all dependencies' licenses" {
  do_external_licenses_check \

  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }

      contain a complex error message if error_msg does not provide enough
      information. In that case, set suppress_default_error to True.
    error_msg: (String) String with one and only one '%s'. Formatted with each
      invalid response upon check_success(input) failure.
    suppress_default_error: (Bool) Suppress the above error message in favor of
      one from the check_success function.

internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access

// it will be stopped and joined.
TF_CAPI_EXPORT extern void TF_DeleteServer(TF_Server* server);

// Register a listener method that processes printed messages.
//
// If any listeners are registered, the print operator will call all listeners
// with the printed messages and immediately return without writing to the
// logs.
TF_CAPI_EXPORT extern void TF_RegisterLogListener(
    void (*listener)(const char*));

    cat $BATS_TEST_TMPDIR/actual_missing_licenses
  fi

  # Fail if either of the two "extras" or "missing" lists are present. If so,
  # then the user will see the above error messages.
  [[ ! -s $BATS_TEST_TMPDIR/actual_extra_licenses ]] && [[ ! -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]
}

@test "Pip package generated license includes all dependencies' licenses" {
  do_external_licenses_check \

  void AsyncWait(TFE_Context* context, TF_Status* status) const;

  // Device strings for component devices that only include a
  // worker/task/replica if any of those differ across components. Useful for
  // printing debug messages.
  std::vector<std::string> SummarizeDeviceNames() const;

 private:
  // A sequence of device names, indicating which devices replicated operations
  // are forwarded to.