parameters: (node, **kwargs)
return_statement: return nodes
docstring: Map MXNet's broadcast_not_equal operator attributes to onnx's Equal operator
      and return the created node.
docstring_summary: Map MXNet's broadcast_not_equal operator attributes to onnx's Equal operator
      and return the created node.
docstring_tokens: Map MXNet s broadcast_not_equal operator attributes to onnx s Equal operator and return the created node .
function: def convert_broadcast_not_equal(node, **kwargs):
      """Map MXNet's broadcast_not_equal operator attributes to onnx's Equal operator
      and return the created node.
      """
      from onnx.helper import make_node
      name, input_nodes, _ = get_inputs(node, kwargs)
      input_dtypes = get_input_dtypes(node, kwargs)
  
      dtype = input_dtypes[0]
      dtype_t = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[dtype]
  
      nodes = [
          make_node("Equal", input_nodes, [name+"_equal"]),
          make_node("Not", [name+"_equal"], [name+"_not"]),
          make_node("Cast", [name+"_not"], [name], name=name, to=int(dtype_t))
      ]
      return nodes
function_tokens: def convert_broadcast_not_equal ( node , * * kwargs ) : from onnx . helper import make_node name , input_nodes , _ = get_inputs ( node , kwargs ) input_dtypes = get_input_dtypes ( node , kwargs ) dtype = input_dtypes [ 0 ] dtype_t = onnx . mapping . NP_TYPE_TO_TENSOR_TYPE [ dtype ] nodes = [ make_node ( "Equal" , input_nodes , [ name + "_equal" ] ) , make_node ( "Not" , [ name + "_equal" ] , [ name + "_not" ] ) , make_node ( "Cast" , [ name + "_not" ] , [ name ] , name = name , to = int ( dtype_t ) ) ] return nodes