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/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

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

#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) {

				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,
		}

			expectedRespStatus: http.StatusBadRequest,
			lifecycleResponse:  []byte(``),
			errorResponse: APIErrorResponse{
				Resource: SlashSeparator + bucketName + SlashSeparator,
				Code:     "InvalidArgument",
				Message:  "Filter must have exactly one of Prefix, Tag, or And specified",
			},

			shouldPass: false,
		},
		// Date contains wrong format
		{
			method:             http.MethodPut,

  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 (ctx == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid Context");
    return nullptr;
  }

  tensorflow::AbstractTensorInterface* t =
      tensorflow::unwrap(ctx)->CreateTensor(
          static_cast<tensorflow::DataType>(dtype), dimvec);

  if (t == nullptr) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported dtype: ", dtype);
    return nullptr;
  }

    return absl::OkStatus();
  }

  absl::Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  absl::Status AddInputList(

		writeErrorResponseJSON(ctx, w, errorCodes.ToAPIErrWithErr(ErrBadRequest, InvalidArgument{
			Bucket: bucket,
			Err:    fmt.Errorf("Remote target ARN %s missing or ineligible for replication resync", arn),
		}), r.URL)
		return
	}

	if len(tgtArns) > 1 && arn == "" {
		writeErrorResponseJSON(ctx, w, errorCodes.ToAPIErrWithErr(ErrBadRequest, InvalidArgument{
			Bucket: bucket,

	}

	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,
		}
	}