docstr: Generates tokens using the model.
docstr_tok: ["Generates","tokens","using","the","model","."]
code: def generate(
      self,
      inputs: Union[
          Dict[str, torch.Tensor],
          Dict[str, np.ndarray],
          Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
      ],
      mask=None,
  ) -> Dict[str, torch.Tensor]:
      """Generates tokens using the model."""
      log_once(f"For generating text, using: {self.generation}")
      input_ids, _ = self._unpack_inputs(inputs)
  
      with torch.no_grad():
          input_lengths = []
          sequences_list = []
          for input_ids_sample in input_ids:
              input_ids_sample_no_padding = remove_left_padding(
                  input_ids_sample, self.tokenizer
              )
  
              if (
                  input_ids_sample_no_padding.shape[1]
                  > self.max_input_length
              ):
                  logger.warning(
                      f"Input length {input_ids_sample_no_padding.shape[1]} is "
                      f"greater than max input length {self.max_input_length}. Truncating."
                  )
                  input_ids_sample_no_padding = (
                      input_ids_sample_no_padding[
                          :, -self.max_input_length :
                      ]
                  )  # noqa E203
  
              input_lengths.append(input_ids_sample_no_padding.shape[1])
  
              # Wrap with flash attention backend for faster generation
              with torch.backends.cuda.sdp_kernel(
                  enable_flash=True,
                  enable_math=False,
                  enable_mem_efficient=False,
              ) if (
                  torch.cuda.is_available()
                  and self.curr_device.type == "cuda"
              ) else contextlib.nullcontext():
                  # Generate text using the model
                  model_outputs = self.model.generate(
                      input_ids=input_ids_sample_no_padding,
                      attention_mask=mask,
                      generation_config=self.generation,
                      return_dict_in_generate=True,
                      output_scores=True,
                  )
  
              sequences_list.append(model_outputs.sequences[0])
  
          # Extract the predictions, probabilities and logits from the model outputs
          # through the forward pass of the output feature
          outputs = self.output_feature_decoder.decoder_obj.forward(
              sequences_list,
              input_lengths,
              self.max_new_tokens,
          )
  
      return outputs
  
imports: import contextlib
  import logging
  import os
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from transformers import AutoConfig, GenerationConfig
  from ludwig.accounting.used_tokens import get_used_tokens_for_llm
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, MODEL_LLM, PREDICTIONS, TEXT, USED_TOKENS
  from ludwig.features.base_feature import ModuleWrapper, OutputFeature
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.features.text_feature import TextOutputFeature
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.schema.features.base import BaseOutputFeatureConfig, FeatureCollection
  from ludwig.schema.model_types.llm import LLMModelConfig
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.llm_quantization_utils import convert_quantized_linear_to_linear
  from ludwig.utils.llm_utils import add_left_padding, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, initialize_adapter, load_pretrained_from_config, pad_target_tensor_for_fine_tuning, remove_left_padding, to_device
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.output_feature_utils import set_output_feature_tensor
  from ludwig.utils.tokenizers import HFTokenizer
  from ludwig.utils.torch_utils import reg_loss
label_desc: Use image node_id 13 for calling the LLM obj's underlying member method code with example usage: obj.generate(inputs, mask) and returns: outputs