code: def _cat_and_pad(tensors, pad_token_id):
      # used by generate(): tensors is a (batched) list of (candidates, len); len is varied across batch
  
      # Initialize padded tensor with shape ( all_candidates , max_candidate_length ),
      # where all_candidates counted from all inputs
      new_shape = sum([t.shape[0] for t in tensors]), max(
          [t.shape[1] for t in tensors]
      )
      output = tf.fill(new_shape, pad_token_id)
  
      # Normal tensor doesn't support slice assignment, so we need tf.Variable
      output = tf.Variable(output)
  
      # Assign, and then convert back to tensor
      ind = 0
      for t in tensors:
          output[ind : ind + t.shape[0], : t.shape[1]].assign(t)
          ind += t.shape[0]
  
      output = tf.convert_to_tensor(output)
      return tf.cast(output, tensors[0][0][0].dtype)
  
imports: from __future__ import annotations
  import copy
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...configuration_utils import PretrainedConfig
  from ...generation import TFLogitsProcessorList
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, shape_list, unpack_inputs
  from ...utils import ModelOutput, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
label_desc: Use image node_id 9 for calling the TFRagSequenceForGeneration obj's underlying member method code with example usage: obj._cat_and_pad(tensors, pad_token_id) and returns: tf