code: def dot(inputs, axes, normalize=False, **kwargs):
      """Functional interface to the `Dot` layer.
  
      Args:
          inputs: A list of input tensors (at least 2).
          axes: Integer or tuple of integers,
              axis or axes along which to take the dot product.
          normalize: Whether to L2-normalize samples along the
              dot product axis before taking the dot product.
              If set to True, then the output of the dot product
              is the cosine proximity between the two samples.
          **kwargs: Standard layer keyword arguments.
  
      Returns:
          A tensor, the dot product of the samples from the inputs.
      """
      return Dot(axes=axes, normalize=normalize, **kwargs)(inputs)
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
label_desc: Use image node_id 8 for calling a global function with example usage: dot(inputs, axes, normalize) and returns: Dot