Batch normalization.
tf.compat.v1.nn.fused_batch_norm( x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC', is_training=True, name=None, exponential_avg_factor=1.0 )
See Source: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy.
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. The shape and meaning of this argument depends on the value of is_training and exponential_avg_factor as follows: is_trainingFalse (inference): Mean must be a Tensor of the same shape as scale containing the estimated population mean computed during training. is_trainingTrue and exponential_avg_factor == 1.0: Mean must be None. is_trainingTrue and exponential_avg_factor != 1.0: Mean must be a Tensor of the same shape as scale containing the exponential running mean.

variance  A Tensor of 1 dimension for population variance. The shape and meaning of this argument depends on the value of is_training and exponential_avg_factor as follows: is_trainingFalse (inference): Variance must be a Tensor of the same shape as scale containing the estimated population variance computed during training. is_training==True and exponential_avg_factor == 1.0: Variance must be None. is_training==True and exponential_avg_factor != 1.0: Variance must be a Tensor of the same shape as scale containing the exponential running variance. 
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). 
exponential_avg_factor  A float number (usually between 0 and 1) used for controlling the decay of the running population average of mean and variance. If set to 1.0, the current batch average is returned. 
Returns  

y  A 4D Tensor for the normalized, scaled, offsetted x. 
running_mean  A 1D Tensor for the exponential running mean of x. The output value is (1  exponential_avg_factor) * mean + exponential_avg_factor * batch_mean), where batch_mean is the mean of the current batch in x. 
running_var  A 1D Tensor for the exponential running variance The output value is (1  exponential_avg_factor) * variance + exponential_avg_factor * batch_variance), where batch_variance is the variance of the current batch in x. 
Batch Normalization  Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf)
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/nn/fused_batch_norm