parameters: (value, bias, data_format=None, name=None)
docstring: Adds `bias` to `value`.
  
    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 channel 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.
      data_format: A string. 'N...C' and 'NC...' are supported. If `None` (the
        default) is specified then 'N..C' is assumed.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError if data format is unrecognized, if `value` has less than two
      dimensions when `data_format` is 'N..C'/`None` or `value` has less
      then three dimensions when `data_format` is `NC..`, if `bias` does not
      have exactly one dimension (is a vector), or if the size of `bias`
      does not match the size of the channel dimension of `value`.
docstring_summary: Adds `bias` to `value`.
docstring_tokens: Adds bias to value .
function: def bias_add(value, bias, data_format=None, name=None):
    """Adds `bias` to `value`.
  
    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 channel 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.
      data_format: A string. 'N...C' and 'NC...' are supported. If `None` (the
        default) is specified then 'N..C' is assumed.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError if data format is unrecognized, if `value` has less than two
      dimensions when `data_format` is 'N..C'/`None` or `value` has less
      then three dimensions when `data_format` is `NC..`, if `bias` does not
      have exactly one dimension (is a vector), or if the size of `bias`
      does not match the size of the channel dimension of `value`.
    """
    with ops.name_scope(name, "BiasAdd", [value, bias]) as name:
      if data_format is not None:
        if data_format.startswith("NC"):
          data_format = "NCHW"
        elif data_format.startswith("N") and data_format.endswith("C"):
          data_format = "NHWC"
        else:
          raise ValueError("`data_format` must be of the form `N...C` or "
                           f"`NC...`. Received: data_format={data_format}")
  
      if not context.executing_eagerly():
        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(value, bias, data_format=data_format, name=name)
function_tokens: def bias_add ( value , bias , data_format = None , name = None ) : with ops . name_scope ( name , "BiasAdd" , [ value , bias ] ) as name : if data_format is not None : if data_format . startswith ( "NC" ) : data_format = "NCHW" elif data_format . startswith ( "N" ) and data_format . endswith ( "C" ) : data_format = "NHWC" else : raise ValueError ( "`data_format` must be of the form `N...C` or " f"`NC...`. Received: data_format={data_format}" ) if not context . executing_eagerly ( ) : 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 ( value , bias , data_format = data_format , name = name )