def trainer_config_process(self, args):
        """
        Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
        creation.
        """
        # DeepSpeed does:
        # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps
        train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
        self.fill_match(
            "train_micro_batch_size_per_gpu", args.per_device_train_batch_size, "per_device_train_batch_size"
        )
        self.fill_match("gradient_accumulation_steps", args.gradient_accumulation_steps, "gradient_accumulation_steps")
        self.fill_match("train_batch_size", train_batch_size, "train_batch_size (calculated)")
        self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm")

        self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
        self.fill_match("optimizer.params.betas", [args.adam_beta1, args.adam_beta2], "adam_beta1+adam_beta2")
        self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon")
        self.fill_match("optimizer.params.weight_decay", args.weight_decay, "weight_decay")

        self.fill_only("scheduler.params.warmup_min_lr", 0)  # not a trainer arg
        self.fill_match("scheduler.params.warmup_max_lr", args.learning_rate, "learning_rate")
        # total_num_steps - will get set in trainer_config_finalize

        # fp16
        if args.fp16 or args.fp16_full_eval:
            fp16_backend = "apex" if args.fp16_backend == "apex" else "amp"
        else:
            fp16_backend = None

        if args.save_on_each_node:
            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
            self.config["checkpoint"] = self.config.get("checkpoint", {})
            self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node

        # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
        # any here unless the user did the work
        self.fill_match(
            "fp16.enabled",
            ((args.fp16 or args.fp16_full_eval) and fp16_backend == "amp"),
            "fp16|fp16_full_eval+fp16_backend(amp)",
        )

        # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
        # ZeRO features
        self.fill_match("amp.enabled", fp16_backend == "apex", "fp16+fp16_backend(apex)")
        self.fill_match("amp.opt_level", args.fp16_opt_level, "fp16_opt_level")

        self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval")

        # deepspeed's default mode is fp16 unless there is a config that says differently
        if self.is_true("bf16.enabled"):
            self._dtype = torch.bfloat16
        elif self.is_false("fp16.enabled"):
            self._dtype = torch.float32
        else:
            self._dtype = torch.float16

        Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
        creation.
        
dpkd/transformers/src/transformers/integrations/deepspeed.py