Hi @Brando_Miranda,
I dont know if i can help you, but maybe a look at the documentation for BatchNorm 2 or the Python source code for BatchNorm 2 helps.
In the source code it says:
"""Decide whether the mini-batch stats should be used for normalization rather than the buffers.
    Mini-batch stats are used in training mode, and in eval mode when buffers are None.
"""
if self.training:
    bn_training = True
else:
    bn_training = (self.running_mean is None) and (self.running_var is None)

If you want to run your model in eval mode and the BatchNorm layers of the model you are using are initialized with track_running_stats=True you could try setting track_running_stats=False, self.running_mean=None and self.running_mean=None, but I’m not sure if this would work.
Another option would be initializing the BatchNorm layer new with track_running_stats=False and overriding the Parameters for  weight and bias with yours, if affine=True.