Remove dead code related to namespaces (#602)

They were removed in this commit 44f36b5

lib/axon.ex

@@ -3875,7 +3875,7 @@ defmodule Axon do
   ## `init_fn`
 
   The `init_fn` receives two arguments, the input template and
-  an optional map with initial parameters for layers or namespaces:
+  an optional map with initial parameters for layers:
 
       {init_fn, predict_fn} = Axon.build(model)
       init_fn.(Nx.template({1, 1}, {:f, 32}), %{"dense_0" => dense_params})
@@ -3968,7 +3968,7 @@ defmodule Axon do
   purposes.
 
   You may optionally specify initial parameters for some layers or
-  namespaces by passing a partial parameter map:
+  by passing a partial parameter map:
 
       Axon.trace_init(model, %{"dense_0" => dense_params})
 

lib/axon/compiler.ex

@@ -687,98 +687,6 @@ defmodule Axon.Compiler do
     {id, model_funs, cache, op_counts, block_cache, model_state_meta}
   end
 
-  defp recur_model_funs(
-         %Axon.Node{id: id, op: :namespace, name: name_fn, parent: [parent]},
-         nodes,
-         {cache, op_counts, block_cache, model_state_meta},
-         config
-       ) do
-    name = name_fn.(:namespace, op_counts)
-    # To ensure that a namespace always has the same layer names,
-    # we reset op_counts, input layers always belong to the global
-    # namespace, so we include those regardless
-    input_count = op_counts[:input] || 0
-    namespace_op_counts = %{input: input_count}
-    namespace_model_state_meta = %{parameters: %{}, state: %{}, frozen_parameters: %{}}
-
-    # All of the children of this namespace belong to it, so
-    # we forward this name to the namespace, but everything after
-    # it belongs to whatever namespace we're currently in
-    {parent_id, {cache, namespace_op_counts, block_cache, namespace_model_state_meta}} =
-      to_model_funs(
-        parent,
-        nodes,
-        {cache, namespace_op_counts, block_cache, namespace_model_state_meta},
-        config
-      )
-
-    # Update the global op_count of input layers, since they
-    # are a global operation regardless of where they are
-    input_count = namespace_op_counts[:input] || 0
-    op_counts = Map.put(op_counts, :input, input_count)
-
-    # Update the model state meta to include the namespace model state meta
-    model_state_meta =
-      model_state_meta
-      |> Map.update!(:parameters, &Map.put(&1, name, namespace_model_state_meta[:parameters]))
-      |> Map.update!(:state, &Map.put(&1, name, namespace_model_state_meta[:state]))
-      |> Map.update!(
-        :frozen_parameters,
-        &Map.put(&1, name, namespace_model_state_meta[:frozen_parameters])
-      )
-
-    # The function just returns the result of it's child,
-    # or parent depending on how you view the tree
-    predict_fun = fn params, inputs, state, cache, result_cache, fn_stacktrace ->
-      # We're only concerned with this namespaces parameters, so we pair
-      # down parameters first given the namespace
-      namespace_params = params[name]
-
-      # TODO: How should hooks be handled here?
-      # TODO: I think we can actually handle parameter freezing and access
-      # better here by only forwarding params[namespace] to the child function
-      {out, {state, result_cache}} =
-        call_predict_cache(
-          parent_id,
-          namespace_params,
-          inputs,
-          state,
-          cache,
-          result_cache,
-          fn_stacktrace
-        )
-
-      state =
-        if map_size(state) == 0 do
-          state
-        else
-          %{name => state}
-        end
-
-      {out, {state, result_cache}}
-    end
-
-    init_fun = fn template, cache, result_cache, fn_stacktrace, keys ->
-      {_parent_template, {namespace_params, result_cache}} =
-        call_init_cache(parent_id, template, %{}, cache, result_cache, fn_stacktrace, keys)
-
-      params =
-        if namespace_params == %{} do
-          %{}
-        else
-          %{name => namespace_params}
-        end
-
-      {pred_expr, {_, result_cache}} =
-        predict_fun.(params, template, %{}, cache, result_cache, fn_stacktrace)
-
-      {Nx.to_template(pred_expr), {params, result_cache}}
-    end
-
-    model_funs = %{predict: predict_fun, init: init_fun}
-    {id, model_funs, cache, op_counts, block_cache, model_state_meta}
-  end
-
   defp recur_model_funs(
          %Axon.Node{
            id: id,