code: def dense_product_fixup(
      x,
      reduced_dims,
      new_dims,
      kernel_initializer,
      activation_functions=None,
      name="dense_product",
      **kwargs,
  ):
      """Wrapper around dense_product that is explicit about kernel initialization.
  
      Args:
        x: a Tensor
        reduced_dims: a list of Dimensions.
        new_dims: a list of Dimensions.
        kernel_initializer: The kernel initializer to use for the dense product. For
          fixup, this is the initializer scaled according to the number of encoder
          and decoder layers.
        activation_functions: a list of activation functions (or a singleton)
          Each can be a either: - a callable function from Tensor to Tensor - a
            string function name from namespace mtf) - None or "linear", meaning no
            activation function
        name: an optional string
        **kwargs: additional kwargs for mtf.layers.dense()
  
      Returns:
        Component wise product of dense layers with fixup init.
      """
      return mtf.layers.dense_product(
          x,
          reduced_dims,
          new_dims,
          activation_functions,
          name,
          kernel_initializer=kernel_initializer,
          **kwargs,
      )
  
imports: import math
  import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import attention
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: dense_product_fixup(x, reduced_dims, new_dims, kernel_initializer, activation_functions, name) and returns: mtf