parameters: (target, to_append)
return_statement: return sparse_tensor.SparseTensorValue(
        indices=new_indices, values=new_values, dense_shape=new_dense_shape)
docstring: Append sparse tensor value objects.
docstring_summary: Append sparse tensor value objects.
docstring_tokens: Append sparse tensor value objects .
function: def _append_sparse_tensor_value(target, to_append):
    """Append sparse tensor value objects."""
    # Make sure the sparse tensors are of the same size (except for the 0th dim).
    if len(target.dense_shape) != len(to_append.dense_shape):
      raise RuntimeError(
          'Unable to concatenate %s and %s. The inner dense shapes do not '
          'have the same number of dimensions (%s vs %s)' %
          (target, to_append, target.dense_shape, to_append.dense_shape))
  
    if target.dense_shape[1:] != to_append.dense_shape[1:]:
      raise RuntimeError(
          'Unable to concatenate %s and %s. The inner dense shapes do not '
          'match inner dimensions (%s vs %s)' %
          (target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))
  
    # Add the to_append indices to target, updating the 0th value, and keeping
    # track of the maximum so we know the final dense_shape of this tensor.
    base_dim0_value = target.dense_shape[0]
    max_dim0_value = target.dense_shape[0]
    new_indices = target.indices
    for index in to_append.indices:
      # Here, we iterate through the sparse indices of the tensor to append. For
      # each index, we update its zeroth value (the batch index) by adding the
      # number of batch items in the tensor we are appending to (so an index
      # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim
      # size 3 would become [3, 0, 1].)
      index[0] += base_dim0_value
      max_dim0_value = max(max_dim0_value, index[0])
      new_indices = np.append(new_indices, [index], axis=0)
  
    # Extend the values array to contain all of the appended values. These will
    # be in the same order as the indices added above.
    new_values = np.concatenate((target.values, to_append.values), axis=0)
  
    # Create a new dense shape by replacing the value for the 0th dimension
    # with the new max dim0 value.
    new_dense_shape = list(target.dense_shape)
    new_dense_shape[0] = max_dim0_value + 1
    new_dense_shape = tuple(new_dense_shape)
  
    return sparse_tensor.SparseTensorValue(
        indices=new_indices, values=new_values, dense_shape=new_dense_shape)
function_tokens: def _append_sparse_tensor_value ( target , to_append ) : # Make sure the sparse tensors are of the same size (except for the 0th dim). if len ( target . dense_shape ) != len ( to_append . dense_shape ) : raise RuntimeError ( 'Unable to concatenate %s and %s. The inner dense shapes do not ' 'have the same number of dimensions (%s vs %s)' % ( target , to_append , target . dense_shape , to_append . dense_shape ) ) if target . dense_shape [ 1 : ] != to_append . dense_shape [ 1 : ] : raise RuntimeError ( 'Unable to concatenate %s and %s. The inner dense shapes do not ' 'match inner dimensions (%s vs %s)' % ( target , to_append , target . dense_shape [ 1 : ] , to_append . dense_shape [ 1 : ] ) ) # Add the to_append indices to target, updating the 0th value, and keeping # track of the maximum so we know the final dense_shape of this tensor. base_dim0_value = target . dense_shape [ 0 ] max_dim0_value = target . dense_shape [ 0 ] new_indices = target . indices for index in to_append . indices : # Here, we iterate through the sparse indices of the tensor to append. For # each index, we update its zeroth value (the batch index) by adding the # number of batch items in the tensor we are appending to (so an index # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim # size 3 would become [3, 0, 1].) index [ 0 ] += base_dim0_value max_dim0_value = max ( max_dim0_value , index [ 0 ] ) new_indices = np . append ( new_indices , [ index ] , axis = 0 ) # Extend the values array to contain all of the appended values. These will # be in the same order as the indices added above. new_values = np . concatenate ( ( target . values , to_append . values ) , axis = 0 ) # Create a new dense shape by replacing the value for the 0th dimension # with the new max dim0 value. new_dense_shape = list ( target . dense_shape ) new_dense_shape [ 0 ] = max_dim0_value + 1 new_dense_shape = tuple ( new_dense_shape ) return sparse_tensor . SparseTensorValue ( indices = new_indices , values = new_values , dense_shape = new_dense_shape )