original_docstring: 
      Custom collation function for collate with new implementation of individual samples in data pipeline
      
docstring: Custom collation function for collate with new implementation of individual samples in data pipeline
docstring_tokens: Custom collation function for collate with new implementation of individual samples in data pipeline
code: def individual_collate(batch):
      """
      Custom collation function for collate with new implementation of individual samples in data pipeline
      """
  
      data = batch
  
      # Assuming there's at least one instance in the batch
      add_data_keys = data[0].keys()
      collected_data = {k: [] for k in add_data_keys}
  
      for i in range(len(list(data))):
          for k in add_data_keys:
              collected_data[k].append(data[i][k])
  
      for k in add_data_keys:
          collected_data[k] = torch.stack(collected_data[k])
  
      return collected_data
code_tokens: def individual_collate ( batch ) : data = batch add_data_keys = data [ 0 ] . keys ( ) collected_data = { k : [ ] for k in add_data_keys } for i in range ( len ( list ( data ) ) ) : for k in add_data_keys : collected_data [ k ] . append ( data [ i ] [ k ] ) for k in add_data_keys : collected_data [ k ] = torch . stack ( collected_data [ k ] ) return collected_data
short_docstring: Custom collation function for collate with new implementation of individual samples in data pipeline
short_docstring_tokens: Custom collation function for collate with new implementation of individual samples in data pipeline
comment: """
      Custom collation function for collate with new implementation of individual samples in data pipeline
      """$SEP$# Assuming there's at least one instance in the batch