code: def batch_norm_with_global_normalization(
      t=None,
      m=None,
      v=None,
      beta=None,
      gamma=None,
      variance_epsilon=None,
      scale_after_normalization=None,
      name=None,
      input=None,  # pylint: disable=redefined-builtin
      mean=None,
      variance=None,
  ):
      """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).
        input: Alias for t.
        mean: Alias for m.
        variance: Alias for v.
  
      Returns:
         A batch-normalized `t`.
  
      References:
        Batch Normalization - Accelerating Deep Network Training by Reducing
        Internal Covariate Shift:
          [Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
          ([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
      """
      t = deprecated_argument_lookup("input", input, "t", t)
      m = deprecated_argument_lookup("mean", mean, "m", m)
      v = deprecated_argument_lookup("variance", variance, "v", v)
      return batch_normalization(
          t,
          m,
          v,
          beta,
          gamma if scale_after_normalization else None,
          variance_epsilon,
          name,
      )
  
imports: import math
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import candidate_sampling_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import ctc_ops
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gen_sparse_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_fused_batch_norm_grad
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import device_context
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.deprecation import deprecated_args
  from tensorflow.python.util.deprecation import deprecated_argument_lookup
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 25 for calling a global function with example usage: batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name, input, mean, variance) and returns: batch_normalization