parameters: (node, **kwargs)
return_statement: return [node]
docstring: Map MXNet's random_normal operator attributes to onnx's RandomNormal
      operator and return the created node.
docstring_summary: Map MXNet's random_normal operator attributes to onnx's RandomNormal
      operator and return the created node.
docstring_tokens: Map MXNet s random_normal operator attributes to onnx s RandomNormal operator and return the created node .
function: def convert_random_normal(node, **kwargs):
      """Map MXNet's random_normal operator attributes to onnx's RandomNormal
      operator and return the created node.
      """
      name, input_nodes, attrs = get_inputs(node, kwargs)
  
      # Converting to float32
      mean = float(attrs.get("loc", 0))
      scale = float(attrs.get("scale", 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(
          'RandomNormal',
          input_nodes,
          [name],
          mean=mean,
          scale=scale,
          dtype=dtype,
          shape=shape,
          name=name
      )
      return [node]
function_tokens: def convert_random_normal ( node , * * kwargs ) : name , input_nodes , attrs = get_inputs ( node , kwargs ) # Converting to float32 mean = float ( attrs . get ( "loc" , 0 ) ) scale = float ( attrs . get ( "scale" , 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 ( 'RandomNormal' , input_nodes , [ name ] , mean = mean , scale = scale , dtype = dtype , shape = shape , name = name ) return [ node ]