parameters: (x, scale, offset,  # pylint: disable=invalid-name
                       mean=None,
                       variance=None,
                       epsilon=0.001,
                       data_format="NHWC",
                       is_training=True,
                       name=None)
return_statement: return y, batch_mean, batch_var
docstring: r"""Batch normalization.
  
    As described in http://arxiv.org/abs/1502.03167.
  
    Args:
      x: Input `Tensor` of 4 dimensions.
      scale: A `Tensor` of 1 dimension for scaling.
      offset: A `Tensor` of 1 dimension for bias.
      mean: A `Tensor` of 1 dimension for population mean used for inference.
      variance: A `Tensor` of 1 dimension for population variance
                used for inference.
      epsilon: A small float number added to the variance of x.
      data_format: The data format for x. Either "NHWC" (default) or "NCHW".
      is_training: A bool value to specify if the operation is used for
                   training or inference.
      name: A name for this operation (optional).
  
    Returns:
      y: A 4D Tensor for the normalized, scaled, offsetted x.
      batch_mean: A 1D Tensor for the mean of x.
      batch_var: A 1D Tensor for the variance of x.
  
    Raises:
      ValueError: If mean or variance is not None when is_training is True.
docstring_summary: r"""Batch normalization.
docstring_tokens: r Batch normalization .
function: def fused_batch_norm(x, scale, offset,  # pylint: disable=invalid-name
                       mean=None,
                       variance=None,
                       epsilon=0.001,
                       data_format="NHWC",
                       is_training=True,
                       name=None):
    r"""Batch normalization.
  
    As described in http://arxiv.org/abs/1502.03167.
  
    Args:
      x: Input `Tensor` of 4 dimensions.
      scale: A `Tensor` of 1 dimension for scaling.
      offset: A `Tensor` of 1 dimension for bias.
      mean: A `Tensor` of 1 dimension for population mean used for inference.
      variance: A `Tensor` of 1 dimension for population variance
                used for inference.
      epsilon: A small float number added to the variance of x.
      data_format: The data format for x. Either "NHWC" (default) or "NCHW".
      is_training: A bool value to specify if the operation is used for
                   training or inference.
      name: A name for this operation (optional).
  
    Returns:
      y: A 4D Tensor for the normalized, scaled, offsetted x.
      batch_mean: A 1D Tensor for the mean of x.
      batch_var: A 1D Tensor for the variance of x.
  
    Raises:
      ValueError: If mean or variance is not None when is_training is True.
    """
    x = ops.convert_to_tensor(x, name="input")
    scale = ops.convert_to_tensor(scale, name="scale")
    offset = ops.convert_to_tensor(offset, name="offset")
    if is_training:
      if (mean is not None) or (variance is not None):
        raise ValueError("Both 'mean' and 'variance' must be None "
                         "if is_training is True.")
    if mean is None:
      mean = constant_op.constant([])
    if variance is None:
      variance = constant_op.constant([])
    y, batch_mean, batch_var, _, _ = gen_nn_ops.fused_batch_norm(
        x,
        scale,
        offset,
        mean,
        variance,
        epsilon=epsilon,
        data_format=data_format,
        is_training=is_training,
        name=name)
    return y, batch_mean, batch_var
function_tokens: def fused_batch_norm ( x , scale , offset , # pylint: disable=invalid-name mean = None , variance = None , epsilon = 0.001 , data_format = "NHWC" , is_training = True , name = None ) : x = ops . convert_to_tensor ( x , name = "input" ) scale = ops . convert_to_tensor ( scale , name = "scale" ) offset = ops . convert_to_tensor ( offset , name = "offset" ) if is_training : if ( mean is not None ) or ( variance is not None ) : raise ValueError ( "Both 'mean' and 'variance' must be None " "if is_training is True." ) if mean is None : mean = constant_op . constant ( [ ] ) if variance is None : variance = constant_op . constant ( [ ] ) y , batch_mean , batch_var , _ , _ = gen_nn_ops . fused_batch_norm ( x , scale , offset , mean , variance , epsilon = epsilon , data_format = data_format , is_training = is_training , name = name ) return y , batch_mean , batch_var