"tf_buffer.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_buffer_internal",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:platform_port",
        "//tensorflow/core/platform:protobuf",
        "//tensorflow/core/platform:status",
    ],
)

tf_cuda_library(
    name = "tf_buffer_internal",

    TracingOperationPtr operation(CreateOperation());
    TF_RETURN_IF_ERROR(operation->Reset("Placeholder", nullptr));
    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(

    TF_SetStatus(&s, static_cast<TF_Code>(result.error_code()),
                 std::string(result.error_message()).data());
    if (TF_GetCode(&s) != TF_Code::TF_OK) {
      tensorflow::CoordinationServiceError error;
      *error.mutable_source_task() = result.error_payload().source_task();
      TF_SetPayload(&s, tensorflow::CoordinationErrorPayloadKey().data(),
                    error.SerializeAsString().c_str());
    }

    // Get transpose attrs
    bool t_a;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a));

    bool t_b;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b));

    // Conj each input
    AbstractTensorHandle* conj_output;
    std::string name = "Conj_A_MatMul_Grad";
    TF_RETURN_IF_ERROR(
        SafeConj(ctx, forward_inputs_[0], &conj_output, name.c_str()));

    bool clear_existing_contexts) {
#if defined(IS_MOBILE_PLATFORM)
  status->status = tensorflow::errors::Unimplemented(
      "TFE_ContextSetServerDef not supported on mobile");
#else   // !defined(IS_MOBILE_PLATFORM)
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }

        ":abstract_operation",
        ":c_api_unified_internal",
        "//tensorflow/c/experimental/gradients/tape:tape_operation",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
    ],
)

cc_library(
    name = "gradients_internal",
    srcs = [
        "gradients.cc",
    ],
    hdrs = [
        "gradients.h",
        "gradients_internal.h",
    ],

          op_stack.insert(op_stack.begin(), op_id);
        }
      }
    }
  }
  if (!state.op_tape.empty()) {
    return tensorflow::errors::Internal("Invalid tape state.");
  }
  if (result.size() != source_tensor_ids.size()) {
    return errors::Internal("Expected result Span to be of size ",
                            source_tensor_ids.size(), " found ", result.size(),

        if [[ ! -f $target ]] && [[ ! -d $target ]]; then
            echo "$i" >> errors.txt
        fi
        if [[ -e errors.txt ]]; then
            echo "Broken links found:"
            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF

  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);

static void RenameFile(const TF_Filesystem* filesystem, const char* src,
                       const char* dst, TF_Status* status) {
  // If target is a directory return TF_FAILED_PRECONDITION.
  // Target might be missing, so don't error in that case.
  struct stat st;
  if (stat(dst, &st) != 0) {
    if (errno != ENOENT) {
      TF_SetStatusFromIOError(status, errno, dst);
      return;
    }
  } else if (S_ISDIR(st.st_mode)) {