parameters: (self, namespace_prefix, original_input_name,
                                  reshape_dims_name, reduction_dims_name)
return_statement: return quantize_input_name, min_output_name, max_output_name
docstring: Takes one float input to an op, and converts it to quantized form.
docstring_summary: Takes one float input to an op, and converts it to quantized form.
docstring_tokens: Takes one float input to an op and converts it to quantized form .
function: def eightbitize_input_to_node(self, namespace_prefix, original_input_name,
                                  reshape_dims_name, reduction_dims_name):
      """Takes one float input to an op, and converts it to quantized form."""
      unique_input_name = unique_node_name_from_input(original_input_name)
      reshape_input_name = namespace_prefix + "_reshape_" + unique_input_name
      min_input_name = namespace_prefix + "_min_" + unique_input_name
      max_input_name = namespace_prefix + "_max_" + unique_input_name
      quantize_input_name = namespace_prefix + "_quantize_" + unique_input_name
      reshape_input_node = create_node("Reshape", reshape_input_name,
                                       [original_input_name, reshape_dims_name])
      set_attr_dtype(reshape_input_node, "T", tf.float32)
      self.add_output_graph_node(reshape_input_node)
      min_input_node = create_node("Min", min_input_name, [reshape_input_name,
                                                           reduction_dims_name])
      set_attr_dtype(min_input_node, "T", tf.float32)
      set_attr_bool(min_input_node, "keep_dims", False)
      self.add_output_graph_node(min_input_node)
      max_input_node = create_node("Max", max_input_name, [reshape_input_name,
                                                           reduction_dims_name])
      set_attr_dtype(max_input_node, "T", tf.float32)
      set_attr_bool(max_input_node, "keep_dims", False)
      self.add_output_graph_node(max_input_node)
      quantize_input_node = create_node("QuantizeV2", quantize_input_name,
                                        [original_input_name, min_input_name,
                                         max_input_name])
      set_attr_dtype(quantize_input_node, "T", tf.quint8)
      set_attr_string(quantize_input_node, "mode", b"MIN_FIRST")
      self.add_output_graph_node(quantize_input_node)
      min_output_name = quantize_input_name + ":1"
      max_output_name = quantize_input_name + ":2"
      return quantize_input_name, min_output_name, max_output_name
function_tokens: def eightbitize_input_to_node ( self , namespace_prefix , original_input_name , reshape_dims_name , reduction_dims_name ) : unique_input_name = unique_node_name_from_input ( original_input_name ) reshape_input_name = namespace_prefix + "_reshape_" + unique_input_name min_input_name = namespace_prefix + "_min_" + unique_input_name max_input_name = namespace_prefix + "_max_" + unique_input_name quantize_input_name = namespace_prefix + "_quantize_" + unique_input_name reshape_input_node = create_node ( "Reshape" , reshape_input_name , [ original_input_name , reshape_dims_name ] ) set_attr_dtype ( reshape_input_node , "T" , tf . float32 ) self . add_output_graph_node ( reshape_input_node ) min_input_node = create_node ( "Min" , min_input_name , [ reshape_input_name , reduction_dims_name ] ) set_attr_dtype ( min_input_node , "T" , tf . float32 ) set_attr_bool ( min_input_node , "keep_dims" , False ) self . add_output_graph_node ( min_input_node ) max_input_node = create_node ( "Max" , max_input_name , [ reshape_input_name , reduction_dims_name ] ) set_attr_dtype ( max_input_node , "T" , tf . float32 ) set_attr_bool ( max_input_node , "keep_dims" , False ) self . add_output_graph_node ( max_input_node ) quantize_input_node = create_node ( "QuantizeV2" , quantize_input_name , [ original_input_name , min_input_name , max_input_name ] ) set_attr_dtype ( quantize_input_node , "T" , tf . quint8 ) set_attr_string ( quantize_input_node , "mode" , b"MIN_FIRST" ) self . add_output_graph_node ( quantize_input_node ) min_output_name = quantize_input_name + ":1" max_output_name = quantize_input_name + ":2" return quantize_input_name , min_output_name , max_output_name