code: def apply_padding(
      batch: List[Mapping[str, Union[Dict[str, Tensor], Tensor]]],
      opts: argparse.Namespace,
      key: Optional[str] = None,
  ) -> List[Mapping[str, Tensor]]:
      """
      Apply padding to make samples the same length.
  
      The input is a list of dictionaries of the form:
          [{"samples": @entry, ...}, ...].
      If @key is specified, @entry has the form {@key: @value}, where @value
      corresponds to the entry that should be padded. Otherwise, @entry is assumed
      to be a tensor.
  
      The tensor mentioned in the above paragraph will have shape [batch_size,
          sequence_length, ...].
  
      Args:
          batch: The batch of data.
          opts: The global arguments.
          key: The key of the sample element to pad. If @key is None, the entry
              is assumed to be a tensor.
  
      Returns:
          The modified batch of size [batch_size, padded_sequence_length, ...].
      """
  
      def get_entry(
          entry: Union[Dict[str, Tensor], Tensor], key: Optional[str]
      ) -> Tensor:
          """
          Helper function to deal with the cases where entries in the samples.
  
          Args:
              entry: Either a tensor of shape [batch_size, sequence_length, ...],
                  or a dictionary containing {@key: tensor of shape
                  [batch_size, sequence_length, ...]}.
          Returns:
              A tensor of shape [batch_size, ...].
          """
          if isinstance(entry, dict):
              return entry[key]
          if key is not None:
              raise ValueError(
                  f"Key should not be specified if entries are not dicts."
              )
          return entry
  
      if get_entry(batch[0]["samples"], key).dim() != 1:
          # Padding only applies to 1d tensors.
          return batch
      padding_idx = getattr(
          opts, "model.classification.byteformer.padding_index"
      )
      # Tensors have shape [batch_size, sequence_length, ...]. Get the maximum
      # sequence length.
      padded_seq_len = max(
          get_entry(be["samples"], key).shape[0] for be in batch
      )
      for elem in batch:
          sample = get_entry(
              elem["samples"], key
          )  # [batch_size, sequence_length, ...].
          sample = functional.pad(
              sample,
              (0, padded_seq_len - sample.shape[0]),
              value=padding_idx,
          )  # [batch_size, padded_sequence_length, ...].
          if isinstance(elem["samples"], dict):
              elem["samples"][key] = sample
          else:
              elem["samples"] = sample
      return batch
  
file_path: ml-cvnets/data/collate_fns/byteformer_collate_functions.py
imports: import argparse
  from typing import Dict, List, Mapping, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from data.collate_fns import COLLATE_FN_REGISTRY, collate_functions
  from data.transforms import audio_bytes, image_bytes
label_desc: Use image node_id 2 for calling a global function with example usage: apply_padding(batch, opts, key) and returns: batch, entry, batch, entry