class EncoderRNN([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):
    def __init__(self, input_size, hidden_size, dropout_p=0.1):
        super([EncoderRNN](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module"), self).__init__()
        self.hidden_size = hidden_size

        self.embedding = [nn.Embedding](https://docs.pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding "torch.nn.Embedding")(input_size, hidden_size)
        self.gru = [nn.GRU](https://docs.pytorch.org/docs/stable/generated/torch.nn.GRU.html#torch.nn.GRU "torch.nn.GRU")(hidden_size, hidden_size, batch_first=True)
        self.dropout = [nn.Dropout](https://docs.pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout "torch.nn.Dropout")(dropout_p)

    def forward(self, input):
        embedded = self.dropout(self.embedding(input))
        output, hidden = self.gru(embedded)
        return output, hidden