def batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name=None):
    'Batch normalization.\n\n  This op is deprecated. See `tf.nn.batch_normalization`.\n\n  Args:\n    t: A 4D input Tensor.\n    m: A 1D mean Tensor with size matching the last dimension of t.\n      This is the first output from tf.nn.moments,\n      or a saved moving average thereof.\n    v: A 1D variance Tensor with size matching the last dimension of t.\n      This is the second output from tf.nn.moments,\n      or a saved moving average thereof.\n    beta: A 1D beta Tensor with size matching the last dimension of t.\n      An offset to be added to the normalized tensor.\n    gamma: A 1D gamma Tensor with size matching the last dimension of t.\n      If "scale_after_normalization" is true, this tensor will be multiplied\n      with the normalized tensor.\n    variance_epsilon: A small float number to avoid dividing by 0.\n    scale_after_normalization: A bool indicating whether the resulted tensor\n      needs to be multiplied with gamma.\n    name: A name for this operation (optional).\n\n   Returns:\n     A batch-normalized `t`.\n  '
    return batch_normalization(t, m, v, beta, (gamma if scale_after_normalization else None), variance_epsilon, name)
Batch normalization.

This op is deprecated. See `tf.nn.batch_normalization`.

Args:
  t: A 4D input Tensor.
  m: A 1D mean Tensor with size matching the last dimension of t.
    This is the first output from tf.nn.moments,
    or a saved moving average thereof.
  v: A 1D variance Tensor with size matching the last dimension of t.
    This is the second output from tf.nn.moments,
    or a saved moving average thereof.
  beta: A 1D beta Tensor with size matching the last dimension of t.
    An offset to be added to the normalized tensor.
  gamma: A 1D gamma Tensor with size matching the last dimension of t.
    If "scale_after_normalization" is true, this tensor will be multiplied
    with the normalized tensor.
  variance_epsilon: A small float number to avoid dividing by 0.
  scale_after_normalization: A bool indicating whether the resulted tensor
    needs to be multiplied with gamma.
  name: A name for this operation (optional).

 Returns:
   A batch-normalized `t`.
def batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name=None):
    'Batch normalization.\n\n  This op is deprecated. See `tf.nn.batch_normalization`.\n\n  Args:\n    t: A 4D input Tensor.\n    m: A 1D mean Tensor with size matching the last dimension of t.\n      This is the first output from tf.nn.moments,\n      or a saved moving average thereof.\n    v: A 1D variance Tensor with size matching the last dimension of t.\n      This is the second output from tf.nn.moments,\n      or a saved moving average thereof.\n    beta: A 1D beta Tensor with size matching the last dimension of t.\n      An offset to be added to the normalized tensor.\n    gamma: A 1D gamma Tensor with size matching the last dimension of t.\n      If "scale_after_normalization" is true, this tensor will be multiplied\n      with the normalized tensor.\n    variance_epsilon: A small float number to avoid dividing by 0.\n    scale_after_normalization: A bool indicating whether the resulted tensor\n      needs to be multiplied with gamma.\n    name: A name for this operation (optional).\n\n   Returns:\n     A batch-normalized `t`.\n  '
    return batch_normalization(t, m, v, beta, (gamma if scale_after_normalization else None), variance_epsilon, name)