class_docstr: The PPOTrainer uses Proximal Policy Optimization to optimise language models.
  Note, this trainer is heavily inspired by the original OpenAI learning to summarize
  work here: https://github.com/openai/summarize-from-feedback
  
  Attributes:
      **cfg** (`LLM Studio Config`) -- Experiment configuration object. Check the
          documentation of `LLM Studio Config` for more details.
      **model** (`PreTrainedModelWrapper`) -- Model to be optimized, Hugging Face
          transformer model with a value head. Check the documentation of
          `PreTrainedModelWrapper` for more details.
      **tokenizer** (`Union[PreTrainedTokenizer, PreTrainedTokenizerFast]`)
          Tokenizer to be used for encoding the data. Check the documentation of
          `transformers.PreTrainedTokenizer` and
          `transformers.PreTrainedTokenizerFast` for more details.
      **optimizer** (`torch.optim.Optimizer`) -- Optimizer to be used for training.
      **lr_scheduler** (`torch.optim.lr_scheduler`) -- Learning rate scheduler to be
          used for training.
      **scaler** (`torch.cuda.amp.GradScaler`) -- Gradient scaler to be used for
          training.
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code: def prepare_model_inputs(
      self, queries: torch.Tensor, responses: torch.Tensor
  ):
      input_ids = [
          torch.cat([q, r]) for q, r in zip(queries, responses)
      ]
      input_data = self.data_collator(
          [
              {"input_ids": ids, "attention_mask": torch.ones_like(ids)}
              for ids in input_ids
          ]
      ).to(self.current_device)
  
      input_data.pop(
          "labels", None
      )  # we don't want to compute LM losses
  
      return input_data
  
imports: import math
  import time
  import warnings
  from collections.abc import Mapping
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from datasets import Dataset
  from huggingface_hub import PyTorchModelHubMixin
  from torch.cuda.amp import autocast
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizer, PreTrainedTokenizerFast
  from llm_studio.src.utils.modeling_utils import unwrap_model
label_desc: Use image node_id 4 for calling the PPOTrainer obj's underlying member method code with example usage: obj.prepare_model_inputs(queries, responses) and returns: input_data