parameters: (value, bias, name=None)
docstring: Adds `bias` to `value`.
  
    This is a deprecated version of bias_add and will soon to be removed.
  
    This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
    Broadcasting is supported, so `value` may have any number of dimensions.
    Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
    case where both types are quantized.
  
    Args:
      value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
        `int16`, `int8`, `complex64`, or `complex128`.
      bias: A 1-D `Tensor` with size matching the last dimension of `value`.
        Must be the same type as `value` unless `value` is a quantized type,
        in which case a different quantized type may be used.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
docstring_summary: Adds `bias` to `value`.
docstring_tokens: Adds bias to value .
function: def bias_add_v1(value, bias, name=None):
    """Adds `bias` to `value`.
  
    This is a deprecated version of bias_add and will soon to be removed.
  
    This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
    Broadcasting is supported, so `value` may have any number of dimensions.
    Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
    case where both types are quantized.
  
    Args:
      value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
        `int16`, `int8`, `complex64`, or `complex128`.
      bias: A 1-D `Tensor` with size matching the last dimension of `value`.
        Must be the same type as `value` unless `value` is a quantized type,
        in which case a different quantized type may be used.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
    """
    with ops.op_scope([value, bias], name, "BiasAddV1") as name:
      value = ops.convert_to_tensor(value, name="input")
      bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
      return gen_nn_ops._bias_add_v1(value, bias, name=name)
function_tokens: def bias_add_v1 ( value , bias , name = None ) : with ops . op_scope ( [ value , bias ] , name , "BiasAddV1" ) as name : value = ops . convert_to_tensor ( value , name = "input" ) bias = ops . convert_to_tensor ( bias , dtype = value . dtype , name = "bias" ) return gen_nn_ops . _bias_add_v1 ( value , bias , name = name )