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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 construct_graph(self, fixed, feedable, x_val, hash_key):
 """ Construct the graph required to run the attack through generate_np. :param fixed: Structural elements that require defining a new graph. :param feedable: Arguments that can be fed to the same graph when they take different values. :param x_val: symbolic adversarial example :param hash_key: the key used to store this graph in our cache """

    # try our very best to create a TF placeholder for each of the
    # feedable keyword arguments, and check the types are one of
    # the allowed types
    class_name = str(self.__class__).split(".")[-1][:-2]
    _logger.info("Constructing new graph for attack " + class_name)

    # remove the None arguments, they are just left blank
    for k in list(feedable.keys()):
      if feedable[k] is None:
        del feedable[k]

    # process all of the rest and create placeholders for them
    new_kwargs = dict(x for x in fixed.items())
    for name, value in feedable.items():
      given_type = value.dtype
      if isinstance(value, np.ndarray):
        if value.ndim == 0:
          # This is pretty clearly not a batch of data
          new_kwargs[name] = tf.placeholder(given_type, shape=[], name=name)
        else:
          # Assume that this is a batch of data, make the first axis variable
          # in size
          new_shape = [None] + list(value.shape[1:])
          new_kwargs[name] = tf.placeholder(given_type, new_shape, name=name)
      elif isinstance(value, utils.known_number_types):
        new_kwargs[name] = tf.placeholder(given_type, shape=[], name=name)
      else:
        raise ValueError("Could not identify type of argument " +
                         name + ": " + str(value))

    # x is a special placeholder we always want to have
    x_shape = [None] + list(x_val.shape)[1:]
    x = tf.placeholder(self.tf_dtype, shape=x_shape)

    # now we generate the graph that we want
    x_adv = self.generate(x, **new_kwargs)

    self.graphs[hash_key] = (x, new_kwargs, x_adv)

    if len(self.graphs) >= 10:
      warnings.warn("Calling generate_np() with multiple different "
                    "structural parameters is inefficient and should"
                    " be avoided. Calling generate() is preferred.")