instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
                used to warp the prediction score distribution of the language modeling head applied before multinomial
                sampling at each generation step.
            max_length (`int`, *optional*, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`Union[int, List[int]]`, *optional*):
                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
            seed (`List[int]`, *optional*):
                Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
                `seed` argument from stateless functions in `tf.random`.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
                encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation.TFSampleDecoderOnlyOutput`], [`~generation.TFSampleEncoderDecoderOutput`] or `tf.Tensor`: A
            `tf.Tensor` containing the generated tokens (default behaviour) or a
            [`~generation.TFSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation.TFSampleEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.

        Examples:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import (
        ...     AutoTokenizer,
        ...     TFAutoModelForCausalLM,
        ...     TFLogitsProcessorList,
        ...     TFMinLengthLogitsProcessor,
        ...     TFTopKLogitsWarper,
        ...     TFTemperatureLogitsWarper,
        ... )

        >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
        >>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")

        >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
        >>> model.generation_config.pad_token_id = model.generation_config.eos_token_id

        >>> input_prompt = "Today is a beautiful day, and"
        >>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids

        >>> # instantiate logits processors
        >>> logits_processor = TFLogitsProcessorList(
        ...     [
        ...         TFMinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
        ...     ]
        ... )
        >>> # instantiate logits processors
        >>> logits_warper = TFLogitsProcessorList(
        ...     [
        ...         TFTopKLogitsWarper(50),
        ...         TFTemperatureLogitsWarper(0.7),
        ...     ]
        ... )

        >>> tf.random.set_seed(0)
        >>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Today is a beautiful day, and I love my country. But when I look at Donald Trump,']
        ```Nr