class_docstr: A container that holds info about partial batches for `predict()`.
docstr: Pads out the batch dimension of a tensor to the complete batch size.
code: def pad_batch(self, *dataset_batch_elements):
      """Pads out the batch dimension of a tensor to the complete batch size."""
  
      def _pad(batch):
          """Helper function to pad nested data within each batch elements."""
          padded_dict_batch = {}
          if isinstance(batch, dict):
              for key, value in batch.items():
                  padded_dict_batch[key] = _pad(value)
              return padded_dict_batch
  
          rank = len(batch.shape)
          assert rank > 0
          missing_count = (
              self.padded_batch_size - self.get_real_batch_size(batch)
          )
          padding = backend.stack(
              [[0, missing_count]] + [[0, 0]] * (rank - 1)
          )
          return array_ops.pad(batch, padding, "constant")
  
      if len(dataset_batch_elements) == 1:
          return _pad(dataset_batch_elements[0])
  
      batch_elements = []
      for batch_element in dataset_batch_elements:
          batch_elements.append(_pad(batch_element))
      return tuple(batch_elements)
  
file_path: tensorflow/tensorflow/python/keras/engine/partial_batch_padding_handler.py
imports: import numpy
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras import backend
  from tensorflow.python.ops import array_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 4 for calling the PartialBatchPaddingHandler obj's underlying member method code with example usage: obj.pad_batch() and returns: tuple, array_ops, _pad, padded_dict_batch