docstr: Predict the class (or classes) of the received text using the
  serialized transformers checkpoint.
  Args:
      input_batch (list): List of Text Tensors from the pre-process function is passed here
  Returns:
      list : It returns a list of the predicted value for the input text
docstr_tok: ["Predict","the","class","(","or","classes",")","of","the","received","text","using","the","serialized","transformers","checkpoint",".","Args",":","input_batch","(","list",")",":","List","of","Text","Tensors","from","the","pre-process","function","is","passed","here","Returns",":","list",":","It","returns","a","list","of","the","predicted","value","for","the","input","text"]
code: def inference(self, input_batch):
      """Predict the class (or classes) of the received text using the
      serialized transformers checkpoint.
      Args:
          input_batch (list): List of Text Tensors from the pre-process function is passed here
      Returns:
          list : It returns a list of the predicted value for the input text
      """
      input_ids_batch, attention_mask_batch = input_batch
      num_inferences = len(input_ids_batch)
      batch_size = int(self.setup_config.get("batch_size", "1"))
  
      # insert padding if a partial batch was received
      padding = batch_size - num_inferences
      if padding > 0:
          pad = torch.nn.ConstantPad1d((0, 0, 0, padding), value=0)
          input_ids_batch = pad(input_ids_batch)
          attention_mask_batch = pad(attention_mask_batch)
  
      inferences = super().inference(
          (input_ids_batch, attention_mask_batch)
      )
  
      return inferences[:num_inferences]
  
imports: import os
  import torch
  from Transformer_handler_generalized import TransformersSeqClassifierHandler
label_desc: Use image node_id 1 for calling the TransformersSeqClassifierNeuronHandler obj's underlying member method code with example usage: obj.inference(input_batch) and returns: inferences