code: def batch_concat(inputs: types.NestedTensor) -> tf.Tensor:
      """Concatenate a collection of Tensors while preserving the batch dimension.
  
      This takes a potentially nested collection of tensors, flattens everything
      but the batch (first) dimension, and concatenates along the resulting data
      (second) dimension.
  
      Args:
        inputs: a tensor or nested collection of tensors.
  
      Returns:
        A concatenated tensor which maintains the batch dimension but concatenates
        all other data along the flattened second dimension.
      """
      flat_leaves = tree.map_structure(snt.Flatten(), inputs)
      return tf.concat(tree.flatten(flat_leaves), axis=-1)
  
imports: import functools
  from typing import List, Optional, Union
  from acme import types
  from acme.utils import tree_utils
  import sonnet
  import tensorflow
  import tree
label_desc: Use image node_id 3 for calling a global function with example usage: batch_concat(inputs) and returns: tf