-# define strstr(haystack, needle) \
-  (__extension__ (__builtin_constant_p (needle) && __string2_1bptr_p (needle) \
-		  ? (((const char *) (needle))[0] == '\0'		      \
-		     ? (char *) (size_t) (haystack)			      \
-		     : (((const char *) (needle))[1] == '\0'		      \
-			? strchr (haystack,				      \
-				  ((const char *) (needle))[0]) 	      \
-			: strstr (haystack, needle)))			      \

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/types.h"

using tensorflow::dyn_cast;
using tensorflow::string;
using tensorflow::gtl::ArraySlice;

namespace tensorflow {
namespace tracing {
namespace graph {

    // Special-cased operation for packing per-device tensors into one parallel
    // tensor.
    if (inputs.size() != parallel_device.num_underlying_devices()) {
      std::string message(absl::StrCat(
          "The parallel device ", parallel_device_name, " expected ",
          parallel_device.num_underlying_devices(),
          " inputs to TPUReplicatedInput, but got ", inputs.size()));

    int port = tensorflow::testing::PickUnusedPortOrDie();
    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost:", port)});
  }
  auto* config = server_def.mutable_default_session_config();
  config->mutable_experimental()->set_collective_group_leader(
      tensorflow::strings::StrCat("/job:", job_name, "/replica:0/task:", 0));
  auto* rewrite_options =

  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
                                    const std::vector<float>& expected_values) {

      session->graph,
      tensorflow::strings::StrCat("fifo_queue_enqueue_", tensor_id).c_str());
  if (enqueue_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the enqueue node in the TF graph.");
    return;
  }

  TF_Operation* placeholder_op = TF_GraphOperationByName(
      session->graph,
      tensorflow::strings::StrCat("arg_tensor_enqueue_", tensor_id).c_str());

  TF_RETURN_IF_ERROR(op->Reset("ZerosLike", /*raw_device_name=*/nullptr));
  if (isa<tracing::TracingOperation>(op.get())) {
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingOperation>(op.get())->SetOpName(
        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(

			};

			const argc = this.argv.length;

			const argvPtrs = [];
			this.argv.forEach((arg) => {
				argvPtrs.push(strPtr(arg));
			});
			argvPtrs.push(0);

			const keys = Object.keys(this.env).sort();
			keys.forEach((key) => {
				argvPtrs.push(strPtr(`${key}=${this.env[key]}`));
			});
			argvPtrs.push(0);

			const argv = offset;
			argvPtrs.forEach((ptr) => {

  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:
      return "CPU:0";
    case DLDeviceType::kDLCUDA:
      return absl::StrCat("GPU:", ctx.device_id);
    case DLDeviceType::kDLROCM:
      return absl::StrCat("GPU:", ctx.device_id);
    default:
      return absl::nullopt;
  }
}

// Converts DLPack data type to TF_DATATYPE.

	VSUMH	V22, V23, V24		// e78670001e64
	VSTRC	V18, V20, V22, V24	// e78240006f8a
	VSTRCB	V18, V20, V22, V24	// e78240006f8a
	VSTRCH	V18, V20, V22, V24	// e78241006f8a
	VSTRCF	V18, V20, V22, V24	// e78242006f8a
	VSTRCBS V18, V20, V22, V24	// e78240106f8a
	VSTRCHS V18, V20, V22, V24	// e78241106f8a
	VSTRCFS V18, V20, V22, V24	// e78242106f8a
	VSTRCZB V18, V20, V22, V24	// e78240206f8a