class_docstr: A generic module for computing convolution on heterogeneous graphs.
  
  The heterograph convolution applies sub-modules on their associating
  relation graphs, which reads the features from source nodes and writes the
  updated ones to destination nodes. If multiple relations have the same
  destination node types, their results are aggregated by the specified method.
  If the relation graph has no edge, the corresponding module will not be called.
  
  Pseudo-code:
  
  .. code::
  
      outputs = {nty : [] for nty in g.dsttypes}
      # Apply sub-modules on their associating relation graphs in parallel
      for relation in g.canonical_etypes:
          stype, etype, dtype = relation
          dstdata = relation_submodule(g[relation], ...)
          outputs[dtype].append(dstdata)
  
      # Aggregate the results for each destination node type
      rsts = {}
      for ntype, ntype_outputs in outputs.items():
          if len(ntype_outputs) != 0:
              rsts[ntype] = aggregate(ntype_outputs)
      return rsts
  
  Examples
  --------
  
  Create a heterograph with three types of relations and nodes.
  
  >>> import dgl
  >>> g = dgl.heterograph({
  ...     ('user', 'follows', 'user') : edges1,
  ...     ('user', 'plays', 'game') : edges2,
  ...     ('store', 'sells', 'game')  : edges3})
  
  Create a ``HeteroGraphConv`` that applies different convolution modules to
  different relations. Note that the modules for ``'follows'`` and ``'plays'``
  do not share weights.
  
  >>> import dgl.nn.pytorch as dglnn
  >>> conv = dglnn.HeteroGraphConv({
  ...     'follows' : dglnn.GraphConv(...),
  ...     'plays' : dglnn.GraphConv(...),
  ...     'sells' : dglnn.SAGEConv(...)},
  ...     aggregate='sum')
  
  Call forward with some ``'user'`` features. This computes new features for both
  ``'user'`` and ``'game'`` nodes.
  
  >>> import tensorflow as tf
  >>> h1 = {'user' : tf.random.normal((g.num_nodes('user'), 5))}
  >>> h2 = conv(g, h1)
  >>> print(h2.keys())
  dict_keys(['user', 'game'])
  
  Call forward with both ``'user'`` and ``'store'`` features. Because both the
  ``'plays'`` and ``'sells'`` relations will update the ``'game'`` features,
  their results are aggregated by the specified method (i.e., summation here).
  
  >>> f1 = {'user' : ..., 'store' : ...}
  >>> f2 = conv(g, f1)
  >>> print(f2.keys())
  dict_keys(['user', 'game'])
  
  Call forward with some ``'store'`` features. This only computes new features
  for ``'game'`` nodes.
  
  >>> g1 = {'store' : ...}
  >>> g2 = conv(g, g1)
  >>> print(g2.keys())
  dict_keys(['game'])
  
  Call forward with a pair of inputs is allowed and each submodule will also
  be invoked with a pair of inputs.
  
  >>> x_src = {'user' : ..., 'store' : ...}
  >>> x_dst = {'user' : ..., 'game' : ...}
  >>> y_dst = conv(g, (x_src, x_dst))
  >>> print(y_dst.keys())
  dict_keys(['user', 'game'])
  
  Notes
  -----
  
  HeteroGraphConv requires that there is a module for every ``'etype'`` in an input graph.
  If you want to apply HeteroGraphConv to a subset of a graph's ``'etypes'``, you must
  create a new graph using for example :func:`~dgl.edge_type_subgraph()`.
  
  Parameters
  ----------
  mods : dict[str, nn.Module]
      Modules associated with every edge types. The forward function of each
      module must have a `DGLGraph` object as the first argument, and
      its second argument is either a tensor object representing the node
      features or a pair of tensor object representing the source and destination
      node features.
  aggregate : str, callable, optional
      Method for aggregating node features generated by different relations.
      Allowed string values are 'sum', 'max', 'min', 'mean', 'stack'.
      The 'stack' aggregation is performed along the second dimension, whose order
      is deterministic.
      User can also customize the aggregator by providing a callable instance.
      For example, aggregation by summation is equivalent to the follows:
  
      .. code::
  
          def my_agg_func(tensors, dsttype):
              # tensors: is a list of tensors to aggregate
              # dsttype: string name of the destination node type for which the
              #          aggregation is performed
              stacked = tf.stack(tensors, axis=0)
              return tf.reduce_sum(stacked, axis=0)
  
  Attributes
  ----------
  mods : dict[str, nn.Module]
      Modules associated with every edge types.
docstr: Forward computation
  
  Invoke the forward function with each module and aggregate their results.
  
  Parameters
  ----------
  g : DGLGraph
      Graph data.
  inputs : dict[str, Tensor] or pair of dict[str, Tensor]
      Input node features.
  mod_args : dict[str, tuple[any]], optional
      Extra positional arguments for the sub-modules.
  mod_kwargs : dict[str, dict[str, any]], optional
      Extra key-word arguments for the sub-modules.
  
  Returns
  -------
  dict[str, Tensor]
      Output representations for every types of nodes.
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code: def call(self, g, inputs, mod_args=None, mod_kwargs=None):
      """Forward computation
  
      Invoke the forward function with each module and aggregate their results.
  
      Parameters
      ----------
      g : DGLGraph
          Graph data.
      inputs : dict[str, Tensor] or pair of dict[str, Tensor]
          Input node features.
      mod_args : dict[str, tuple[any]], optional
          Extra positional arguments for the sub-modules.
      mod_kwargs : dict[str, dict[str, any]], optional
          Extra key-word arguments for the sub-modules.
  
      Returns
      -------
      dict[str, Tensor]
          Output representations for every types of nodes.
      """
      if mod_args is None:
          mod_args = {}
      if mod_kwargs is None:
          mod_kwargs = {}
      outputs = {nty: [] for nty in g.dsttypes}
      if isinstance(inputs, tuple):
          src_inputs, dst_inputs = inputs
          for stype, etype, dtype in g.canonical_etypes:
              rel_graph = g[stype, etype, dtype]
              if stype not in src_inputs or dtype not in dst_inputs:
                  continue
              dstdata = self.mods[etype](
                  rel_graph,
                  (src_inputs[stype], dst_inputs[dtype]),
                  *mod_args.get(etype, ()),
                  **mod_kwargs.get(etype, {}),
              )
              outputs[dtype].append(dstdata)
      else:
          for stype, etype, dtype in g.canonical_etypes:
              rel_graph = g[stype, etype, dtype]
              if stype not in inputs:
                  continue
              dstdata = self.mods[etype](
                  rel_graph,
                  (inputs[stype], inputs[dtype]),
                  *mod_args.get(etype, ()),
                  **mod_kwargs.get(etype, {}),
              )
              outputs[dtype].append(dstdata)
      rsts = {}
      for nty, alist in outputs.items():
          if len(alist) != 0:
              rsts[nty] = self.agg_fn(alist, nty)
      return rsts
  
imports: import tensorflow
  from tensorflow.keras import layers
label_desc: Use image node_id 2 for calling the HeteroGraphConv obj's underlying member method code with example usage: obj.call(g, inputs, mod_args, mod_kwargs) and returns: rsts