How to load inceptionV3 model weights correctly?

I am trying to reconstruct the architecture of a model that uses inceptionV3 pretrained weights, I read the info from the “tflite” file of the said model.

interpreter = tf.lite.Interpreter(model_path="model_predict.tflite")
all_tensor_details = interpreter.get_tensor_details()

for tensor_item in all_tensor_details:
  print("Weight %s:" % tensor_item["name"])

It is using inceptionV3 weights, which sound like:

Weight InceptionV3/Conv2d_4a_3x3/BatchNorm/FusedBatchNorm:
Weight InceptionV3/Mixed_5b/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm:

Am I loading these weights correctly here?:

class Predict(nn.Module):
  def __init__(self, inception):
    super(Predict, self).__init__()
    model += [inception.Conv2d_4a_3x3]
    model += [tf.nn.batch_normalization(name=None)]
    model += [tf.compat.v1.nn.fused_batch_norm(mean=None, variance=None, epsilon=0.001,
    data_format='NHWC', is_training=True, name=None,

    model += [inception.Mixed_5b]
inception = torchvision.models.inception_v3(pretrained=True)
my_pred = Predict(inception)

By that, I mean, was it right to include the batch normalization and fused batch normalization functions, or are they part of inception.Conv2d_4a_3x3 already? I have never used inceptionV3 before, and I only found this post that helped me a bit:

but there is no explanation about the batch normalization. Plus, for the 2nd weight array, inceptionV3 does not contain anything called “Branch_0” separately, so I am assuming everything, including the batch norms, are part of the same weight. That I want to make sure of.

I’m unsure, if mixing TensorFlow and PyTorch would work, so it would be interesting to hear what your use case is and if a similar approach would work before.
You can find the InceptionV3 definition here and see how each layer is used.