parameters: (
      beta_initializer: Text = "zeros",
      gamma_initializer: Text = "ones",
      data_format: Text = "channels_last",
      momentum: float = 0.99,
      epsilon: float = 1e-3,
      name: Text = "tpu_batch_normalization",
  )
return_statement: return bn_layer
docstring: Build a batch normalization layer.
  
      Args:
        beta_initializer: `str`, beta initializer.
        gamma_initializer: `str`, gamma initializer.
        data_format: `str` either "channels_first" for `[batch, channels, height,
          width]` or "channels_last for `[batch, height, width, channels]`.
        momentum: `float`, momentume of batch norm.
        epsilon: `float`, small value for numerical stability.
        name: the name of the batch normalization layer
  
      Returns:
        A normalized `Tensor` with the same `data_format`.
docstring_summary: Build a batch normalization layer.
docstring_tokens: Build a batch normalization layer .
function: def build_batch_norm(
      beta_initializer: Text = "zeros",
      gamma_initializer: Text = "ones",
      data_format: Text = "channels_last",
      momentum: float = 0.99,
      epsilon: float = 1e-3,
      name: Text = "tpu_batch_normalization",
  ):
      """Build a batch normalization layer.
  
      Args:
        beta_initializer: `str`, beta initializer.
        gamma_initializer: `str`, gamma initializer.
        data_format: `str` either "channels_first" for `[batch, channels, height,
          width]` or "channels_last for `[batch, height, width, channels]`.
        momentum: `float`, momentume of batch norm.
        epsilon: `float`, small value for numerical stability.
        name: the name of the batch normalization layer
  
      Returns:
        A normalized `Tensor` with the same `data_format`.
      """
      axis = 1 if data_format == "channels_first" else -1
  
      bn_layer = BatchNormalization(
          axis=axis,
          momentum=momentum,
          epsilon=epsilon,
          center=True,
          scale=True,
          beta_initializer=beta_initializer,
          gamma_initializer=gamma_initializer,
          name=name,
      )
  
      return bn_layer
function_tokens: def build_batch_norm ( beta_initializer : Text = "zeros" , gamma_initializer : Text = "ones" , data_format : Text = "channels_last" , momentum : float = 0.99 , epsilon : float = 1e-3 , name : Text = "tpu_batch_normalization" , ) : axis = 1 if data_format == "channels_first" else - 1 bn_layer = BatchNormalization ( axis = axis , momentum = momentum , epsilon = epsilon , center = True , scale = True , beta_initializer = beta_initializer , gamma_initializer = gamma_initializer , name = name , ) return bn_layer