Introduce code from Rust monorepo. This is a mess

tts/nemo/collections/nlp/modules/common/classifier.py

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+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Dict, Optional
+
+import torch
+from torch import nn as nn
+
+from nemo.collections.common.parts import transformer_weights_init
+from nemo.core.classes import Exportable, NeuralModule, typecheck
+from nemo.core.neural_types import ChannelType, NeuralType
+
+__all__ = ['Classifier']
+
+
+class Classifier(NeuralModule, Exportable):
+    """
+    A baseclass for modules to perform various classification tasks.
+    """
+
+    @property
+    def input_types(self) -> Optional[Dict[str, NeuralType]]:
+        """
+        Returns definitions of module input ports.
+        We implement it here since all NLP classifiers have the same inputs
+        """
+        return {"hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())}
+
+    def __init__(self, hidden_size: int, dropout: float = 0.0,) -> None:
+        """
+        Initializes the Classifier base module.
+        Args:
+            hidden_size: the size of the hidden dimension
+            dropout: dropout to apply to the input hidden states
+        """
+        super().__init__()
+        self._hidden_size = hidden_size
+        self.dropout = nn.Dropout(dropout)
+
+    def post_init(self, use_transformer_init: bool):
+        """
+        Common post-processing to be called at the end of concrete Classifiers init methods
+        Args:
+          use_transformer_init : whether or not to apply transformer_weights_init
+        """
+        if use_transformer_init:
+            self.apply(lambda module: transformer_weights_init(module, xavier=False))
+
+    def input_example(self):
+        """
+        Generates input examples for tracing etc.
+        Returns:
+            A tuple of input examples.
+        """
+        bs = 8
+        seq = 64
+        sample = next(self.parameters())
+        input_example = torch.randn(bs, seq, self._hidden_size).to(sample.device).to(sample.dtype)
+        return tuple([input_example])
+
+    def save_to(self, save_path: str):
+        """
+        Saves the module to the specified path.
+        Args:
+            save_path: Path to where to save the module.
+        """
+        pass
+
+    @classmethod
+    def restore_from(cls, restore_path: str):
+        """
+        Restores the module from the specified path.
+        Args:
+            restore_path: Path to restore the module from.
+        """
+        pass