code: def potentially_ragged_concat(tensors):
      """Concats `Tensor`s along their first dimension.
  
      Args:
          tensors: List of `Tensor`s.
  
      Returns:
          Concatenation of the inputs along the first dimension -- of type
          `np.ndarray` if all input shapes are compatible, or `tf.RaggedTensor`
          if not.
      """
      if len(tensors) == 1:
          return tensors[0]
      elif isinstance(tensors[0], tf.SparseTensor):
          return tf.sparse.concat(axis=0, sp_inputs=tensors)
      elif isinstance(tensors[0], tf.RaggedTensor):
          return tf.concat(tensors, axis=0)
  
      non_batch_shapes = tf.stack(
          [tf.shape(tensor)[1:] for tensor in tensors]
      )
      constant_dims = tf.math.reduce_all(
          non_batch_shapes == non_batch_shapes[:1], axis=0
      )
      if tf.math.reduce_all(constant_dims).numpy().item():
          # All non-batch dims are constant
          if _is_scalar(tensors[0]):
              return tf.stack(tensors, axis=0)
          else:
              return tf.concat(tensors, axis=0)
  
      # First, identify constant inner dimensions by finding the
      # rightmost dimension that is not constant
      constant_inner_dimensions = (
          constant_dims.numpy().tolist()[::-1].index(False)
      )
      # If there are constant inner dimensions, define a constant inner shape
      if constant_inner_dimensions == 0:
          constant_inner_shape = None
      else:
          constant_inner_shape = tensors[0].shape[
              -constant_inner_dimensions:
          ]
      return tf.ragged.constant(
          [tensor.numpy() for tensor in tensors],
          inner_shape=constant_inner_shape,
      ).merge_dims(0, 1)
  
imports: import contextlib
  import warnings
  import numpy
  import tensorflow
  import tree
  from packaging.version import Version
  from tensorflow.python.eager import context
  from keras import callbacks
  from keras import metrics
  from keras import optimizers
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 12 for calling a global function with example usage: potentially_ragged_concat(tensors) and returns: tf, tensors, tf, tf, tf, tf