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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _make_inputnode(self, frequency):
 """ Generates an input node for the given frequency. It also adds implicit file format conversion nodes to the pipeline. Parameters frequency : str The frequency (i.e. 'per_session', 'per_visit', 'per_subject' or 'per_study') of the input node to retrieve """

        # Check to see whether there are any outputs for the given frequency
        inputs = list(self.frequency_inputs(frequency))
        # Get list of input names for the requested frequency, addding fields
        # to hold iterator IDs
        input_names = [i.name for i in inputs]
        input_names.extend(self.study.FREQUENCIES[frequency])
        if not input_names:
            raise ArcanaError(
                "No inputs to '{}' pipeline for requested freqency '{}'"
                .format(self.name, frequency))
        # Generate input node and connect it to appropriate nodes
        inputnode = self.add('{}_inputnode'.format(frequency),
                             IdentityInterface(fields=input_names))
        # Loop through list of nodes connected to study data specs and
        # connect them to the newly created input node
        for input in inputs:  # @ReservedAssignment
            # Keep track of previous conversion nodes to avoid replicating the
            # conversion for inputs that are used in multiple places
            prev_conv_nodes = {}
            for (node, node_in, format,  # @ReservedAssignment @IgnorePep8
                 conv_kwargs) in self._input_conns[input.name]:
                # If fileset formats differ between study and pipeline
                # inputs create converter node (if one hasn't been already)
                # and connect input to that before connecting to inputnode
                if self.requires_conversion(input, format):
                    try:
                        conv = format.converter_from(input.format,
                                                     **conv_kwargs)
                    except ArcanaNoConverterError as e:
                        e.msg += (
                            "which is required to convert '{}' from {} to {} "
                            "for '{}' input of '{}' node".format(
                                input.name, input.format, format, node_in,
                                node.name))
                        raise e
                    try:
                        in_node = prev_conv_nodes[format.name]
                    except KeyError:
                        in_node = prev_conv_nodes[format.name] = self.add(
                            'conv_{}_to_{}_format'.format(input.name,
                                                          format.name),
                            conv.interface,
                            inputs={conv.input: (inputnode, input.name)},
                            requirements=conv.requirements,
                            mem_gb=conv.mem_gb,
                            wall_time=conv.wall_time)
                    in_node_out = conv.output
                else:
                    in_node = inputnode
                    in_node_out = input.name
                self.connect(in_node, in_node_out, node, node_in)
        # Connect iterator inputs
        for iterator, conns in self._iterator_conns.items():
            # Check to see if this is the right frequency for the iterator
            # input, i.e. if it is the only iterator for this frequency
            if self.study.FREQUENCIES[frequency] == (iterator,):
                for (node, node_in, format) in conns:  # @ReservedAssignment
                    self.connect(inputnode, iterator, node, node_in)
        return inputnode