std::strncpy(buffer, "VALID", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "padding", buffer, std::strlen(buffer));
  // Overwriting value in "buffer", should be fine since TFE_Op
  // shouldn't be holding on to it.
  std::strncpy(buffer, "NHWC", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "data_format", buffer, std::strlen(buffer));

  TFE_OpSetAttrType(op, "T", TF_FLOAT);

// Returns the device of the operation that produced `h`. If `h` was produced by
// a copy, returns the destination device of the copy. Note that the returned
// device name is not always the device holding the tensor handle's memory. If
// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.

        for all the RNG in Keras. We plan to switch on the new code path by
        default in tf 2.8, and the behavior change will likely to cause some
        breakage on user side (eg if the test is checking against some golden
        number). These 3 APIs will allow user to disable and switch back to
        legacy behavior if they prefer. In future (eg TF 2.10), we expect to

TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than
// Tensorflow's CPU implementation through constant folding.
TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();
TF_CAPI_EXPORT void TF_SetXlaConstantFoldingDisabled(
    unsigned char should_enable);

unsigned char TF_GetXlaConstantFoldingDisabled() {
  return static_cast<unsigned char>(
      tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding);
}

void TF_SetXlaConstantFoldingDisabled(unsigned char should_enable) {
  tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding =
      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =