parameters: (x,
                                         gamma,
                                         beta,
                                         reduction_axes,
                                         epsilon=1e-3)
return_statement: return nn.fused_batch_norm(
        x, gamma, beta, epsilon=epsilon, data_format=tf_data_format)
docstring: Fused version of `normalize_batch_in_training`.
  
    Args:
        x: Input tensor or variable.
        gamma: Tensor by which to scale the input.
        beta: Tensor with which to center the input.
        reduction_axes: iterable of integers,
            axes over which to normalize.
        epsilon: Fuzz factor.
  
    Returns:
        A tuple length of 3, `(normalized_tensor, mean, variance)`.
docstring_summary: Fused version of `normalize_batch_in_training`.
docstring_tokens: Fused version of normalize_batch_in_training .
function: def _fused_normalize_batch_in_training(x,
                                         gamma,
                                         beta,
                                         reduction_axes,
                                         epsilon=1e-3):
    """Fused version of `normalize_batch_in_training`.
  
    Args:
        x: Input tensor or variable.
        gamma: Tensor by which to scale the input.
        beta: Tensor with which to center the input.
        reduction_axes: iterable of integers,
            axes over which to normalize.
        epsilon: Fuzz factor.
  
    Returns:
        A tuple length of 3, `(normalized_tensor, mean, variance)`.
    """
    if list(reduction_axes) == [0, 1, 2]:
      normalization_axis = 3
      tf_data_format = 'NHWC'
    else:
      normalization_axis = 1
      tf_data_format = 'NCHW'
  
    if gamma is None:
      gamma = constant_op.constant(
          1.0, dtype=x.dtype, shape=[x.shape[normalization_axis]])
    if beta is None:
      beta = constant_op.constant(
          0.0, dtype=x.dtype, shape=[x.shape[normalization_axis]])
  
    return nn.fused_batch_norm(
        x, gamma, beta, epsilon=epsilon, data_format=tf_data_format)
function_tokens: def _fused_normalize_batch_in_training ( x , gamma , beta , reduction_axes , epsilon = 1e-3 ) : if list ( reduction_axes ) == [ 0 , 1 , 2 ] : normalization_axis = 3 tf_data_format = 'NHWC' else : normalization_axis = 1 tf_data_format = 'NCHW' if gamma is None : gamma = constant_op . constant ( 1.0 , dtype = x . dtype , shape = [ x . shape [ normalization_axis ] ] ) if beta is None : beta = constant_op . constant ( 0.0 , dtype = x . dtype , shape = [ x . shape [ normalization_axis ] ] ) return nn . fused_batch_norm ( x , gamma , beta , epsilon = epsilon , data_format = tf_data_format )