parameters: (target, to_append)
docstring: Helper function to append composite tensors to each other in the 0 axis.
  
    In order to support batching within a fit/evaluate/predict call, we need
    to be able to aggregate within a CompositeTensor. Unfortunately, the CT
    API currently does not make this easy - especially in V1 mode, where we're
    working with CompositeTensor Value objects that have no connection with the
    CompositeTensors that created them.
  
    Args:
      target: CompositeTensor or CompositeTensor value object that will be
        appended to.
      to_append: CompositeTensor or CompositeTensor value object to append to.
        'target'.
  
    Returns:
      A CompositeTensor or CompositeTensor value object.
  
    Raises:
      RuntimeError: if concatenation is not possible.
docstring_summary: Helper function to append composite tensors to each other in the 0 axis.
docstring_tokens: Helper function to append composite tensors to each other in the 0 axis .
function: def _append_composite_tensor(target, to_append):
    """Helper function to append composite tensors to each other in the 0 axis.
  
    In order to support batching within a fit/evaluate/predict call, we need
    to be able to aggregate within a CompositeTensor. Unfortunately, the CT
    API currently does not make this easy - especially in V1 mode, where we're
    working with CompositeTensor Value objects that have no connection with the
    CompositeTensors that created them.
  
    Args:
      target: CompositeTensor or CompositeTensor value object that will be
        appended to.
      to_append: CompositeTensor or CompositeTensor value object to append to.
        'target'.
  
    Returns:
      A CompositeTensor or CompositeTensor value object.
  
    Raises:
      RuntimeError: if concatenation is not possible.
    """
    if type(target) is not type(to_append):
      raise RuntimeError('Unable to concatenate %s and %s' %
                         (type(target), type(to_append)))
  
    # Perform type-specific concatenation.
    # TODO(b/125094323): This should be replaced by a simple call to
    # target.append() that should work on all of the below classes.
  
    # If we're seeing a CompositeTensor here, we know it's because we're in
    # Eager mode (or else we'd have evaluated the CT to a CT Value object
    # already). Therefore, it's safe to call concat() on it without evaluating
    # the result any further. If not - that is, if we're seeing a
    # SparseTensorValue or a RaggedTensorValue - we need to hand-update it
    # since we're outside of the graph anyways.
    if isinstance(target, sparse_tensor.SparseTensor):
      # We need to invoke the sparse version of concatenate here - tf.concat
      # won't work.
      return sparse_ops.sparse_concat(sp_inputs=[target, to_append], axis=0)
    elif isinstance(target, ragged_tensor.RaggedTensor):
      return array_ops.concat([target, to_append], axis=0)
    elif isinstance(target, sparse_tensor.SparseTensorValue):
      return _append_sparse_tensor_value(target, to_append)
    elif isinstance(target, ragged_tensor_value.RaggedTensorValue):
      return _append_ragged_tensor_value(target, to_append)
    else:
      raise RuntimeError('Attempted to concatenate unsupported object %s.' %
                         type(target))
function_tokens: def _append_composite_tensor ( target , to_append ) : if type ( target ) is not type ( to_append ) : raise RuntimeError ( 'Unable to concatenate %s and %s' % ( type ( target ) , type ( to_append ) ) ) # Perform type-specific concatenation. # TODO(b/125094323): This should be replaced by a simple call to # target.append() that should work on all of the below classes. # If we're seeing a CompositeTensor here, we know it's because we're in # Eager mode (or else we'd have evaluated the CT to a CT Value object # already). Therefore, it's safe to call concat() on it without evaluating # the result any further. If not - that is, if we're seeing a # SparseTensorValue or a RaggedTensorValue - we need to hand-update it # since we're outside of the graph anyways. if isinstance ( target , sparse_tensor . SparseTensor ) : # We need to invoke the sparse version of concatenate here - tf.concat # won't work. return sparse_ops . sparse_concat ( sp_inputs = [ target , to_append ] , axis = 0 ) elif isinstance ( target , ragged_tensor . RaggedTensor ) : return array_ops . concat ( [ target , to_append ] , axis = 0 ) elif isinstance ( target , sparse_tensor . SparseTensorValue ) : return _append_sparse_tensor_value ( target , to_append ) elif isinstance ( target , ragged_tensor_value . RaggedTensorValue ) : return _append_ragged_tensor_value ( target , to_append ) else : raise RuntimeError ( 'Attempted to concatenate unsupported object %s.' % type ( target ) )