if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {
    status->status = tensorflow::errors::InvalidArgument(
        "DLPack doesn't support ", handle->TypeString(), " tensor");
    return nullptr;

#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))

				VersionID: vid,
			}
		}
		if !versioned {
			return opts, InvalidArgument{
				Bucket: bucket,
				Object: object,
				Err:    fmt.Errorf("VersionID specified %s, but versioning not enabled on bucket=%s", opts.VersionID, bucket),
			}
		}
	}
	opts, err = putOptsFromHeaders(ctx, hdrs, metadata)
	if err != nil {
		return opts, InvalidArgument{
			Bucket: bucket,
			Object: object,
			Err:    err,
		}

		Code:           "InvalidArgument",
		Description:    "Argument maxKeys must be an integer between 0 and 2147483647",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrInvalidEncodingMethod: {
		Code:           "InvalidArgument",
		Description:    "Invalid Encoding Method specified in Request",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrInvalidMaxParts: {
		Code:           "InvalidArgument",

                                      run_options->data, run_options->length)) {
      status->status = InvalidArgument("Unparseable RunOptions proto");
      return;
    }
    if (run_metadata != nullptr && run_metadata->data != nullptr) {
      status->status =
          InvalidArgument("Passing non-empty run_metadata is invalid.");
      return;
    }

    RunMetadata run_metadata_proto;

    factories_sorted.insert(factory.first);
  const char* comma = "";
  for (const string& factory : factories_sorted) {
    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

static TracingContext* CreateTracingExecutionContext(const char* fn_name,
                                                     TF_Status* s) {
  if (default_factory) {

  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  } else if (context->GetContextId() ==
             tensorflow::EagerContext::kInvalidContextId) {
    status->status = tensorflow::errors::InvalidArgument(
        "Trying to update a context with invalid context id.");
  }
  status->status =

	}

	if r.APIVersion != batchExpireAPIVersion {
		return batchExpireJobError{
			Code:           "InvalidArgument",
			Description:    "Unsupported batch expire API version",
			HTTPStatusCode: http.StatusBadRequest,
		}
	}

	if r.Bucket == "" {
		return batchExpireJobError{
			Code:           "InvalidArgument",
			Description:    "Bucket argument missing",
			HTTPStatusCode: http.StatusBadRequest,
		}
	}

  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 2) {
    return errors::InvalidArgument(
        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",
                                     dim_sizes[i],

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

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {