How to use ctranslate2 - 10 common examples

def __init__(self):
        self.layer_norm = common_spec.LayerNormSpec()
        self.linear_0 = common_spec.LinearSpec()
        self.linear_1 = common_spec.LinearSpec()

def set_multi_head_attention_v2(spec, variables, scope, self_attention=False):
    set_layer_norm(spec.layer_norm, variables, "%s/input_layer_norm" % scope)
    if self_attention:
        split_layers = [common_spec.LinearSpec() for _ in range(3)]
        set_linear(split_layers[0], variables, "%s/layer/linear_queries" % scope)
        set_linear(split_layers[1], variables, "%s/layer/linear_keys" % scope)
        set_linear(split_layers[2], variables, "%s/layer/linear_values" % scope)
        utils.fuse_linear(spec.linear[0], split_layers)
    else:
        set_linear(spec.linear[0], variables, "%s/layer/linear_queries" % scope)
        split_layers = [common_spec.LinearSpec() for _ in range(2)]
        set_linear(split_layers[0], variables, "%s/layer/linear_keys" % scope)
        set_linear(split_layers[1], variables, "%s/layer/linear_values" % scope)
        utils.fuse_linear(spec.linear[1], split_layers)
    set_linear(spec.linear[-1], variables, "%s/layer/linear_output" % scope)

def set_multi_head_attention(spec, variables, scope, self_attention=False):
    if self_attention:
        split_layers = [common_spec.LinearSpec() for _ in range(3)]
        set_linear(split_layers[0], variables, "%s.linear_query" % scope)
        set_linear(split_layers[1], variables, "%s.linear_keys" % scope)
        set_linear(split_layers[2], variables, "%s.linear_values" % scope)
        utils.fuse_linear(spec.linear[0], split_layers)
    else:
        set_linear(spec.linear[0], variables, "%s.linear_query" % scope)
        split_layers = [common_spec.LinearSpec() for _ in range(2)]
        set_linear(split_layers[0], variables, "%s.linear_keys" % scope)
        set_linear(split_layers[1], variables, "%s.linear_values" % scope)
        utils.fuse_linear(spec.linear[1], split_layers)
    set_linear(spec.linear[-1], variables, "%s.final_linear" % scope)

def set_multi_head_attention_v2(spec, variables, scope, self_attention=False):
    set_layer_norm(spec.layer_norm, variables, "%s/input_layer_norm" % scope)
    if self_attention:
        split_layers = [common_spec.LinearSpec() for _ in range(3)]
        set_linear(split_layers[0], variables, "%s/layer/linear_queries" % scope)
        set_linear(split_layers[1], variables, "%s/layer/linear_keys" % scope)
        set_linear(split_layers[2], variables, "%s/layer/linear_values" % scope)
        utils.fuse_linear(spec.linear[0], split_layers)
    else:
        set_linear(spec.linear[0], variables, "%s/layer/linear_queries" % scope)
        split_layers = [common_spec.LinearSpec() for _ in range(2)]
        set_linear(split_layers[0], variables, "%s/layer/linear_keys" % scope)
        set_linear(split_layers[1], variables, "%s/layer/linear_values" % scope)
        utils.fuse_linear(spec.linear[1], split_layers)
    set_linear(spec.linear[-1], variables, "%s/layer/linear_output" % scope)

def __init__(self, num_layers):
        self.embeddings = common_spec.EmbeddingsSpec()
        self.position_encodings = PositionEncoderSpec()
        self.layer_norm = common_spec.LayerNormSpec()
        self.projection = common_spec.LinearSpec()
        self.layer = [
            TransformerDecoderLayerSpec() for _ in range(num_layers)]

def __init__(self):
        self.layer_norm = common_spec.LayerNormSpec()
        self.linear_0 = common_spec.LinearSpec()
        self.linear_1 = common_spec.LinearSpec()

def __init__(self, num_layers):
        self.embeddings = common_spec.EmbeddingsSpec()
        self.position_encodings = PositionEncoderSpec()
        self.layer_norm = common_spec.LayerNormSpec()
        self.layer = [TransformerEncoderLayerSpec() for _ in range(num_layers)]

def __init__(self, num_layers):
        self.embeddings = common_spec.EmbeddingsSpec()
        self.position_encodings = PositionEncoderSpec()
        self.layer_norm = common_spec.LayerNormSpec()
        self.layer = [TransformerEncoderLayerSpec() for _ in range(num_layers)]

def __init__(self):
        self.layer_norm = common_spec.LayerNormSpec()
        self.linear_0 = common_spec.LinearSpec()
        self.linear_1 = common_spec.LinearSpec()

def set_multi_head_attention(spec, variables, scope, self_attention=False):
    if self_attention:
        split_layers = [common_spec.LinearSpec() for _ in range(3)]
        set_linear(split_layers[0], variables, "%s.linear_query" % scope)
        set_linear(split_layers[1], variables, "%s.linear_keys" % scope)
        set_linear(split_layers[2], variables, "%s.linear_values" % scope)
        utils.fuse_linear(spec.linear[0], split_layers)
    else:
        set_linear(spec.linear[0], variables, "%s.linear_query" % scope)
        split_layers = [common_spec.LinearSpec() for _ in range(2)]
        set_linear(split_layers[0], variables, "%s.linear_keys" % scope)
        set_linear(split_layers[1], variables, "%s.linear_values" % scope)
        utils.fuse_linear(spec.linear[1], split_layers)
    set_linear(spec.linear[-1], variables, "%s.final_linear" % scope)