code: def FusedBatchNormV3(context, node):
      # Get attributes
      data_format = node.attr.get("data_format", "NHWC")
      epsilon = node.attr.get("epsilon", None)
  
      # Get inputs
      x = context[node.inputs[0]]
      scale = context[node.inputs[1]]
      offset = context[node.inputs[2]]
      mean = context[node.inputs[3]]
      variance = context[node.inputs[4]]
      if data_format == "NHWC":
          # TF's FusedBatchNorm is only for 4D inputs
          x = _transpose_NHWC_to_NCHW(x)
          x = mb.batch_norm(
              x=x,
              mean=mean,
              variance=variance,
              gamma=scale,
              beta=offset,
              epsilon=epsilon,
          )
          x = _transpose_NCHW_to_NHWC(x, node.name)
      else:
          x = mb.batch_norm(
              x=x,
              mean=mean,
              variance=variance,
              gamma=scale,
              beta=offset,
              epsilon=epsilon,
              name=node.name,
          )
      # Inference only batch norm does not have meaningful outputs for
      # batch_mean, batch_variance etc.
      context.add(node.name, x)
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/mil/frontend/tensorflow2/ops.py
imports: import numpy
  from coremltools.converters.mil.frontend.tensorflow.ops import _transpose_NHWC_to_NCHW, _transpose_NCHW_to_NHWC
  from coremltools.converters.mil.frontend.tensorflow.ops import *
  from coremltools.converters.mil.frontend.tensorflow.dialect_ops import *
label_desc: Use image node_id 1 for calling a global function with example usage: FusedBatchNormV3(context, node) without return types