code: def convert_random_uniform(node, **kwargs):
      
      name, input_nodes, attrs = get_inputs(node, kwargs)
  
      # Converting to float32
      low = float(attrs.get("low", 0))
      high = float(attrs.get("high", 1.0))
      shape = convert_string_to_list(attrs.get('shape', '[]'))
      dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
  
      node = onnx.helper.make_node(
          'RandomUniform',
          input_nodes,
          [name],
          low=low,
          high=high,
          dtype=dtype,
          shape=shape,
          name=name
      )
      return [node]
docs: Map MXNet's random_uniform operator attributes to onnx's RandomUniform
      operator and return the created node.