program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.33.5"}, {"coremlc-version", "1877.40.3"}})]
{
    func main<ios16>(tensor<fp32, [1, 77]> input_ids) {
            tensor<int32, []> var_5 = const()[name = tensor<string, []>("op_5"), val = tensor<int32, []>(-1)];
            tensor<bool, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<bool, []>(false)];
            tensor<string, []> cast_1_dtype_0 = const()[name = tensor<string, []>("cast_1_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, []> inputs_embeds_axis_0 = const()[name = tensor<string, []>("inputs_embeds_axis_0"), val = tensor<int32, []>(0)];
            tensor<int32, []> inputs_embeds_batch_dims_0 = const()[name = tensor<string, []>("inputs_embeds_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [49408, 1280]> text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor<fp16, [49408, 1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<int32, [1, 77]> cast_2 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor<string, []>("cast_2")];
            tensor<fp16, [1, 77, 1280]> inputs_embeds_cast = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_2, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor<string, []>("inputs_embeds_cast")];
            tensor<fp16, [1, 77, 1280]> position_embeddings_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [98560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126484608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126583232))), name = tensor<string, []>("position_embeddings_to_fp16_palettized"), shape = tensor<uint32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 1280]> input_3_cast = add(x = inputs_embeds_cast, y = position_embeddings_to_fp16_palettized)[name = tensor<string, []>("input_3_cast")];
            tensor<int32, [1]> hidden_states_1_axes_0 = const()[name = tensor<string, []>("hidden_states_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126583808)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126586432)))];
            tensor<fp16, []> var_15_to_fp16 = const()[name = tensor<string, []>("op_15_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 77, 1280]> hidden_states_1_cast = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast)[name = tensor<string, []>("hidden_states_1_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126589056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128227520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128228096)))];
            tensor<fp16, [1, 77, 1280]> linear_0_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_0_cast")];
            tensor<fp16, []> var_149_to_fp16 = const()[name = tensor<string, []>("op_149_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_5_cast = mul(x = linear_0_cast, y = var_149_to_fp16)[name = tensor<string, []>("tensor_5_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128230720))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129869184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129869760)))];
            tensor<fp16, [1, 77, 1280]> linear_1_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_1_cast")];
            tensor<int32, [4]> var_154 = const()[name = tensor<string, []>("op_154"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_155_cast = reshape(shape = var_154, x = linear_1_cast)[name = tensor<string, []>("op_155_cast")];
            tensor<int32, [4]> var_156_perm_0 = const()[name = tensor<string, []>("op_156_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129872384))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131510848))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131511424)))];
            tensor<fp16, [1, 77, 1280]> linear_2_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_2_cast")];
            tensor<int32, [4]> var_161 = const()[name = tensor<string, []>("op_161"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_162_cast = reshape(shape = var_161, x = linear_2_cast)[name = tensor<string, []>("op_162_cast")];
            tensor<int32, [4]> var_163_perm_0 = const()[name = tensor<string, []>("op_163_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_170 = const()[name = tensor<string, []>("op_170"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_171_cast = reshape(shape = var_170, x = tensor_5_cast)[name = tensor<string, []>("op_171_cast")];
            tensor<int32, [4]> var_172_perm_0 = const()[name = tensor<string, []>("op_172_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_174 = const()[name = tensor<string, []>("op_174"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_159 = transpose(perm = var_172_perm_0, x = var_171_cast)[name = tensor<string, []>("transpose_159")];
            tensor<fp16, [20, 77, 64]> query_states_1_cast = reshape(shape = var_174, x = transpose_159)[name = tensor<string, []>("query_states_1_cast")];
            tensor<int32, [3]> var_176 = const()[name = tensor<string, []>("op_176"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_158 = transpose(perm = var_156_perm_0, x = var_155_cast)[name = tensor<string, []>("transpose_158")];
            tensor<fp16, [20, 77, 64]> key_states_3_cast = reshape(shape = var_176, x = transpose_158)[name = tensor<string, []>("key_states_3_cast")];
            tensor<int32, [3]> var_178 = const()[name = tensor<string, []>("op_178"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_157 = transpose(perm = var_163_perm_0, x = var_162_cast)[name = tensor<string, []>("transpose_157")];
            tensor<fp16, [20, 77, 64]> value_states_3_cast = reshape(shape = var_178, x = transpose_157)[name = tensor<string, []>("value_states_3_cast")];
            tensor<int32, [3]> var_181_perm_0 = const()[name = tensor<string, []>("op_181_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_1_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_1_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_156 = transpose(perm = var_181_perm_0, x = key_states_3_cast)[name = tensor<string, []>("transpose_156")];
            tensor<fp16, [20, 77, 77]> attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast, y = transpose_156)[name = tensor<string, []>("attn_weights_1_cast")];
            tensor<int32, [4]> var_183 = const()[name = tensor<string, []>("op_183"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_184_cast = reshape(shape = var_183, x = attn_weights_1_cast)[name = tensor<string, []>("op_184_cast")];
            tensor<fp16, [1, 1, 77, 77]> op_58_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5929]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131514048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131520064))), name = tensor<string, []>("op_58_to_fp16_palettized"), shape = tensor<uint32, [4]>([1, 1, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_3_cast = add(x = var_184_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_3_cast")];
            tensor<int32, [3]> var_189 = const()[name = tensor<string, []>("op_189"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_5_cast = reshape(shape = var_189, x = attn_weights_3_cast)[name = tensor<string, []>("input_5_cast")];
            tensor<fp16, [20, 77, 77]> input_7_cast = softmax(axis = var_5, x = input_5_cast)[name = tensor<string, []>("input_7_cast")];
            tensor<bool, []> attn_output_1_transpose_x_0 = const()[name = tensor<string, []>("attn_output_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_1_transpose_y_0 = const()[name = tensor<string, []>("attn_output_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_1_cast = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast, y = value_states_3_cast)[name = tensor<string, []>("attn_output_1_cast")];
            tensor<int32, [4]> var_194 = const()[name = tensor<string, []>("op_194"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_3_cast = reshape(shape = var_194, x = attn_output_1_cast)[name = tensor<string, []>("attn_output_3_cast")];
            tensor<int32, [4]> attn_output_5_perm_0 = const()[name = tensor<string, []>("attn_output_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_197 = const()[name = tensor<string, []>("op_197"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_155 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast)[name = tensor<string, []>("transpose_155")];
            tensor<fp16, [1, 77, 1280]> input_9_cast = reshape(shape = var_197, x = transpose_155)[name = tensor<string, []>("input_9_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131520640))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133159104))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133159680)))];
            tensor<fp16, [1, 77, 1280]> linear_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized, x = input_9_cast)[name = tensor<string, []>("linear_3_cast")];
            tensor<fp16, [1, 77, 1280]> input_11_cast = add(x = input_3_cast, y = linear_3_cast)[name = tensor<string, []>("input_11_cast")];
            tensor<int32, [1]> input_13_axes_0 = const()[name = tensor<string, []>("input_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133162304)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133164928)))];
            tensor<fp16, [1, 77, 1280]> input_13_cast = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast)[name = tensor<string, []>("input_13_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133167552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139721216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139721792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139726976))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_4_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_13_cast)[name = tensor<string, []>("linear_4_cast")];
            tensor<string, []> input_17_mode_0 = const()[name = tensor<string, []>("input_17_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_17_cast = gelu(mode = input_17_mode_0, x = linear_4_cast)[name = tensor<string, []>("input_17_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139727552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146281216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146281792)))];
            tensor<fp16, [1, 77, 1280]> linear_5_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized, x = input_17_cast)[name = tensor<string, []>("linear_5_cast")];
            tensor<fp16, [1, 77, 1280]> input_19_cast = add(x = input_11_cast, y = linear_5_cast)[name = tensor<string, []>("input_19_cast")];
            tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = tensor<string, []>("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146284416)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146287040)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_7_cast = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast)[name = tensor<string, []>("hidden_states_7_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146289664))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147928128))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147928704)))];
            tensor<fp16, [1, 77, 1280]> linear_6_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_6_cast")];
            tensor<fp16, []> var_236_to_fp16 = const()[name = tensor<string, []>("op_236_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_11_cast = mul(x = linear_6_cast, y = var_236_to_fp16)[name = tensor<string, []>("tensor_11_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147931328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149569792))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149570368)))];
            tensor<fp16, [1, 77, 1280]> linear_7_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_7_cast")];
            tensor<int32, [4]> var_241 = const()[name = tensor<string, []>("op_241"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_242_cast = reshape(shape = var_241, x = linear_7_cast)[name = tensor<string, []>("op_242_cast")];
            tensor<int32, [4]> var_243_perm_0 = const()[name = tensor<string, []>("op_243_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149572992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151211456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151212032)))];
            tensor<fp16, [1, 77, 1280]> linear_8_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_8_cast")];
            tensor<int32, [4]> var_248 = const()[name = tensor<string, []>("op_248"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_249_cast = reshape(shape = var_248, x = linear_8_cast)[name = tensor<string, []>("op_249_cast")];
            tensor<int32, [4]> var_250_perm_0 = const()[name = tensor<string, []>("op_250_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_258_cast = reshape(shape = var_257, x = tensor_11_cast)[name = tensor<string, []>("op_258_cast")];
            tensor<int32, [4]> var_259_perm_0 = const()[name = tensor<string, []>("op_259_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_154 = transpose(perm = var_259_perm_0, x = var_258_cast)[name = tensor<string, []>("transpose_154")];
            tensor<fp16, [20, 77, 64]> query_states_3_cast = reshape(shape = var_261, x = transpose_154)[name = tensor<string, []>("query_states_3_cast")];
            tensor<int32, [3]> var_263 = const()[name = tensor<string, []>("op_263"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_153 = transpose(perm = var_243_perm_0, x = var_242_cast)[name = tensor<string, []>("transpose_153")];
            tensor<fp16, [20, 77, 64]> key_states_7_cast = reshape(shape = var_263, x = transpose_153)[name = tensor<string, []>("key_states_7_cast")];
            tensor<int32, [3]> var_265 = const()[name = tensor<string, []>("op_265"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_152 = transpose(perm = var_250_perm_0, x = var_249_cast)[name = tensor<string, []>("transpose_152")];
            tensor<fp16, [20, 77, 64]> value_states_7_cast = reshape(shape = var_265, x = transpose_152)[name = tensor<string, []>("value_states_7_cast")];
            tensor<int32, [3]> var_268_perm_0 = const()[name = tensor<string, []>("op_268_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_7_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_7_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_151 = transpose(perm = var_268_perm_0, x = key_states_7_cast)[name = tensor<string, []>("transpose_151")];
            tensor<fp16, [20, 77, 77]> attn_weights_7_cast = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast, y = transpose_151)[name = tensor<string, []>("attn_weights_7_cast")];
            tensor<int32, [4]> var_270 = const()[name = tensor<string, []>("op_270"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_271_cast = reshape(shape = var_270, x = attn_weights_7_cast)[name = tensor<string, []>("op_271_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_9_cast = add(x = var_271_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_9_cast")];
            tensor<int32, [3]> var_276 = const()[name = tensor<string, []>("op_276"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_21_cast = reshape(shape = var_276, x = attn_weights_9_cast)[name = tensor<string, []>("input_21_cast")];
            tensor<fp16, [20, 77, 77]> input_23_cast = softmax(axis = var_5, x = input_21_cast)[name = tensor<string, []>("input_23_cast")];
            tensor<bool, []> attn_output_7_transpose_x_0 = const()[name = tensor<string, []>("attn_output_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_7_transpose_y_0 = const()[name = tensor<string, []>("attn_output_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_7_cast = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast, y = value_states_7_cast)[name = tensor<string, []>("attn_output_7_cast")];
            tensor<int32, [4]> var_281 = const()[name = tensor<string, []>("op_281"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_9_cast = reshape(shape = var_281, x = attn_output_7_cast)[name = tensor<string, []>("attn_output_9_cast")];
            tensor<int32, [4]> attn_output_11_perm_0 = const()[name = tensor<string, []>("attn_output_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_284 = const()[name = tensor<string, []>("op_284"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_150 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast)[name = tensor<string, []>("transpose_150")];
            tensor<fp16, [1, 77, 1280]> input_25_cast = reshape(shape = var_284, x = transpose_150)[name = tensor<string, []>("input_25_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151214656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152853120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152853696)))];
            tensor<fp16, [1, 77, 1280]> linear_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized, x = input_25_cast)[name = tensor<string, []>("linear_9_cast")];
            tensor<fp16, [1, 77, 1280]> input_27_cast = add(x = input_19_cast, y = linear_9_cast)[name = tensor<string, []>("input_27_cast")];
            tensor<int32, [1]> input_29_axes_0 = const()[name = tensor<string, []>("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152856320)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152858944)))];
            tensor<fp16, [1, 77, 1280]> input_29_cast = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast)[name = tensor<string, []>("input_29_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152861568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159415232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159415808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159420992))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_10_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_29_cast)[name = tensor<string, []>("linear_10_cast")];
            tensor<string, []> input_33_mode_0 = const()[name = tensor<string, []>("input_33_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_33_cast = gelu(mode = input_33_mode_0, x = linear_10_cast)[name = tensor<string, []>("input_33_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159421568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165975232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165975808)))];
            tensor<fp16, [1, 77, 1280]> linear_11_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized, x = input_33_cast)[name = tensor<string, []>("linear_11_cast")];
            tensor<fp16, [1, 77, 1280]> input_35_cast = add(x = input_27_cast, y = linear_11_cast)[name = tensor<string, []>("input_35_cast")];
            tensor<int32, [1]> hidden_states_13_axes_0 = const()[name = tensor<string, []>("hidden_states_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165978432)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165981056)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_13_cast = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast)[name = tensor<string, []>("hidden_states_13_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165983680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167622144))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167622720)))];
            tensor<fp16, [1, 77, 1280]> linear_12_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_12_cast")];
            tensor<fp16, []> var_323_to_fp16 = const()[name = tensor<string, []>("op_323_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_17_cast = mul(x = linear_12_cast, y = var_323_to_fp16)[name = tensor<string, []>("tensor_17_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167625344))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169263808))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169264384)))];
            tensor<fp16, [1, 77, 1280]> linear_13_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_13_cast")];
            tensor<int32, [4]> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_329_cast = reshape(shape = var_328, x = linear_13_cast)[name = tensor<string, []>("op_329_cast")];
            tensor<int32, [4]> var_330_perm_0 = const()[name = tensor<string, []>("op_330_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169267008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170905472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170906048)))];
            tensor<fp16, [1, 77, 1280]> linear_14_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_14_cast")];
            tensor<int32, [4]> var_335 = const()[name = tensor<string, []>("op_335"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_336_cast = reshape(shape = var_335, x = linear_14_cast)[name = tensor<string, []>("op_336_cast")];
            tensor<int32, [4]> var_337_perm_0 = const()[name = tensor<string, []>("op_337_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_344 = const()[name = tensor<string, []>("op_344"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_345_cast = reshape(shape = var_344, x = tensor_17_cast)[name = tensor<string, []>("op_345_cast")];
            tensor<int32, [4]> var_346_perm_0 = const()[name = tensor<string, []>("op_346_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_348 = const()[name = tensor<string, []>("op_348"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_149 = transpose(perm = var_346_perm_0, x = var_345_cast)[name = tensor<string, []>("transpose_149")];
            tensor<fp16, [20, 77, 64]> query_states_5_cast = reshape(shape = var_348, x = transpose_149)[name = tensor<string, []>("query_states_5_cast")];
            tensor<int32, [3]> var_350 = const()[name = tensor<string, []>("op_350"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_148 = transpose(perm = var_330_perm_0, x = var_329_cast)[name = tensor<string, []>("transpose_148")];
            tensor<fp16, [20, 77, 64]> key_states_11_cast = reshape(shape = var_350, x = transpose_148)[name = tensor<string, []>("key_states_11_cast")];
            tensor<int32, [3]> var_352 = const()[name = tensor<string, []>("op_352"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_147 = transpose(perm = var_337_perm_0, x = var_336_cast)[name = tensor<string, []>("transpose_147")];
            tensor<fp16, [20, 77, 64]> value_states_11_cast = reshape(shape = var_352, x = transpose_147)[name = tensor<string, []>("value_states_11_cast")];
            tensor<int32, [3]> var_355_perm_0 = const()[name = tensor<string, []>("op_355_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_13_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_13_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_146 = transpose(perm = var_355_perm_0, x = key_states_11_cast)[name = tensor<string, []>("transpose_146")];
            tensor<fp16, [20, 77, 77]> attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast, y = transpose_146)[name = tensor<string, []>("attn_weights_13_cast")];
            tensor<int32, [4]> var_357 = const()[name = tensor<string, []>("op_357"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_358_cast = reshape(shape = var_357, x = attn_weights_13_cast)[name = tensor<string, []>("op_358_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_15_cast = add(x = var_358_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_15_cast")];
            tensor<int32, [3]> var_363 = const()[name = tensor<string, []>("op_363"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_37_cast = reshape(shape = var_363, x = attn_weights_15_cast)[name = tensor<string, []>("input_37_cast")];
            tensor<fp16, [20, 77, 77]> input_39_cast = softmax(axis = var_5, x = input_37_cast)[name = tensor<string, []>("input_39_cast")];
            tensor<bool, []> attn_output_13_transpose_x_0 = const()[name = tensor<string, []>("attn_output_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_13_transpose_y_0 = const()[name = tensor<string, []>("attn_output_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_13_cast = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast, y = value_states_11_cast)[name = tensor<string, []>("attn_output_13_cast")];
            tensor<int32, [4]> var_368 = const()[name = tensor<string, []>("op_368"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_15_cast = reshape(shape = var_368, x = attn_output_13_cast)[name = tensor<string, []>("attn_output_15_cast")];
            tensor<int32, [4]> attn_output_17_perm_0 = const()[name = tensor<string, []>("attn_output_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_371 = const()[name = tensor<string, []>("op_371"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_145 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast)[name = tensor<string, []>("transpose_145")];
            tensor<fp16, [1, 77, 1280]> input_41_cast = reshape(shape = var_371, x = transpose_145)[name = tensor<string, []>("input_41_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170908672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172547136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172547712)))];
            tensor<fp16, [1, 77, 1280]> linear_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized, x = input_41_cast)[name = tensor<string, []>("linear_15_cast")];
            tensor<fp16, [1, 77, 1280]> input_43_cast = add(x = input_35_cast, y = linear_15_cast)[name = tensor<string, []>("input_43_cast")];
            tensor<int32, [1]> input_45_axes_0 = const()[name = tensor<string, []>("input_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172550336)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172552960)))];
            tensor<fp16, [1, 77, 1280]> input_45_cast = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast)[name = tensor<string, []>("input_45_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172555584))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179109248))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179109824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179115008))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_16_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast)[name = tensor<string, []>("linear_16_cast")];
            tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_49_cast = gelu(mode = input_49_mode_0, x = linear_16_cast)[name = tensor<string, []>("input_49_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179115584))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185669248))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185669824)))];
            tensor<fp16, [1, 77, 1280]> linear_17_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized, x = input_49_cast)[name = tensor<string, []>("linear_17_cast")];
            tensor<fp16, [1, 77, 1280]> input_51_cast = add(x = input_43_cast, y = linear_17_cast)[name = tensor<string, []>("input_51_cast")];
            tensor<int32, [1]> hidden_states_19_axes_0 = const()[name = tensor<string, []>("hidden_states_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185672448)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185675072)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_19_cast = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast)[name = tensor<string, []>("hidden_states_19_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185677696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187316160))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187316736)))];
            tensor<fp16, [1, 77, 1280]> linear_18_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_18_cast")];
            tensor<fp16, []> var_410_to_fp16 = const()[name = tensor<string, []>("op_410_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_23_cast = mul(x = linear_18_cast, y = var_410_to_fp16)[name = tensor<string, []>("tensor_23_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187319360))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188957824))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188958400)))];
            tensor<fp16, [1, 77, 1280]> linear_19_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_19_cast")];
            tensor<int32, [4]> var_415 = const()[name = tensor<string, []>("op_415"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_416_cast = reshape(shape = var_415, x = linear_19_cast)[name = tensor<string, []>("op_416_cast")];
            tensor<int32, [4]> var_417_perm_0 = const()[name = tensor<string, []>("op_417_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188961024))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190599488))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190600064)))];
            tensor<fp16, [1, 77, 1280]> linear_20_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_20_cast")];
            tensor<int32, [4]> var_422 = const()[name = tensor<string, []>("op_422"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_423_cast = reshape(shape = var_422, x = linear_20_cast)[name = tensor<string, []>("op_423_cast")];
            tensor<int32, [4]> var_424_perm_0 = const()[name = tensor<string, []>("op_424_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_432_cast = reshape(shape = var_431, x = tensor_23_cast)[name = tensor<string, []>("op_432_cast")];
            tensor<int32, [4]> var_433_perm_0 = const()[name = tensor<string, []>("op_433_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_435 = const()[name = tensor<string, []>("op_435"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_144 = transpose(perm = var_433_perm_0, x = var_432_cast)[name = tensor<string, []>("transpose_144")];
            tensor<fp16, [20, 77, 64]> query_states_7_cast = reshape(shape = var_435, x = transpose_144)[name = tensor<string, []>("query_states_7_cast")];
            tensor<int32, [3]> var_437 = const()[name = tensor<string, []>("op_437"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_143 = transpose(perm = var_417_perm_0, x = var_416_cast)[name = tensor<string, []>("transpose_143")];
            tensor<fp16, [20, 77, 64]> key_states_15_cast = reshape(shape = var_437, x = transpose_143)[name = tensor<string, []>("key_states_15_cast")];
            tensor<int32, [3]> var_439 = const()[name = tensor<string, []>("op_439"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_142 = transpose(perm = var_424_perm_0, x = var_423_cast)[name = tensor<string, []>("transpose_142")];
            tensor<fp16, [20, 77, 64]> value_states_15_cast = reshape(shape = var_439, x = transpose_142)[name = tensor<string, []>("value_states_15_cast")];
            tensor<int32, [3]> var_442_perm_0 = const()[name = tensor<string, []>("op_442_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_19_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_19_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_141 = transpose(perm = var_442_perm_0, x = key_states_15_cast)[name = tensor<string, []>("transpose_141")];
            tensor<fp16, [20, 77, 77]> attn_weights_19_cast = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast, y = transpose_141)[name = tensor<string, []>("attn_weights_19_cast")];
            tensor<int32, [4]> var_444 = const()[name = tensor<string, []>("op_444"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_445_cast = reshape(shape = var_444, x = attn_weights_19_cast)[name = tensor<string, []>("op_445_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_21_cast = add(x = var_445_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_21_cast")];
            tensor<int32, [3]> var_450 = const()[name = tensor<string, []>("op_450"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_53_cast = reshape(shape = var_450, x = attn_weights_21_cast)[name = tensor<string, []>("input_53_cast")];
            tensor<fp16, [20, 77, 77]> input_55_cast = softmax(axis = var_5, x = input_53_cast)[name = tensor<string, []>("input_55_cast")];
            tensor<bool, []> attn_output_19_transpose_x_0 = const()[name = tensor<string, []>("attn_output_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_19_transpose_y_0 = const()[name = tensor<string, []>("attn_output_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_19_cast = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast, y = value_states_15_cast)[name = tensor<string, []>("attn_output_19_cast")];
            tensor<int32, [4]> var_455 = const()[name = tensor<string, []>("op_455"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_21_cast = reshape(shape = var_455, x = attn_output_19_cast)[name = tensor<string, []>("attn_output_21_cast")];
            tensor<int32, [4]> attn_output_23_perm_0 = const()[name = tensor<string, []>("attn_output_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_458 = const()[name = tensor<string, []>("op_458"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_140 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast)[name = tensor<string, []>("transpose_140")];
            tensor<fp16, [1, 77, 1280]> input_57_cast = reshape(shape = var_458, x = transpose_140)[name = tensor<string, []>("input_57_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190602688))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192241152))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192241728)))];
            tensor<fp16, [1, 77, 1280]> linear_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized, x = input_57_cast)[name = tensor<string, []>("linear_21_cast")];
            tensor<fp16, [1, 77, 1280]> input_59_cast = add(x = input_51_cast, y = linear_21_cast)[name = tensor<string, []>("input_59_cast")];
            tensor<int32, [1]> input_61_axes_0 = const()[name = tensor<string, []>("input_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192244352)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192246976)))];
            tensor<fp16, [1, 77, 1280]> input_61_cast = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast)[name = tensor<string, []>("input_61_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192249600))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198803264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198803840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198809024))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_22_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_61_cast)[name = tensor<string, []>("linear_22_cast")];
            tensor<string, []> input_65_mode_0 = const()[name = tensor<string, []>("input_65_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_65_cast = gelu(mode = input_65_mode_0, x = linear_22_cast)[name = tensor<string, []>("input_65_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198809600))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205363264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205363840)))];
            tensor<fp16, [1, 77, 1280]> linear_23_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized, x = input_65_cast)[name = tensor<string, []>("linear_23_cast")];
            tensor<fp16, [1, 77, 1280]> input_67_cast = add(x = input_59_cast, y = linear_23_cast)[name = tensor<string, []>("input_67_cast")];
            tensor<int32, [1]> hidden_states_25_axes_0 = const()[name = tensor<string, []>("hidden_states_25_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205366464)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205369088)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_25_cast = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast)[name = tensor<string, []>("hidden_states_25_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205371712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207010176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207010752)))];
            tensor<fp16, [1, 77, 1280]> linear_24_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_24_cast")];
            tensor<fp16, []> var_497_to_fp16 = const()[name = tensor<string, []>("op_497_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_29_cast = mul(x = linear_24_cast, y = var_497_to_fp16)[name = tensor<string, []>("tensor_29_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207013376))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208651840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208652416)))];
            tensor<fp16, [1, 77, 1280]> linear_25_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_25_cast")];
            tensor<int32, [4]> var_502 = const()[name = tensor<string, []>("op_502"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_503_cast = reshape(shape = var_502, x = linear_25_cast)[name = tensor<string, []>("op_503_cast")];
            tensor<int32, [4]> var_504_perm_0 = const()[name = tensor<string, []>("op_504_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208655040))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210293504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210294080)))];
            tensor<fp16, [1, 77, 1280]> linear_26_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_26_cast")];
            tensor<int32, [4]> var_509 = const()[name = tensor<string, []>("op_509"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_510_cast = reshape(shape = var_509, x = linear_26_cast)[name = tensor<string, []>("op_510_cast")];
            tensor<int32, [4]> var_511_perm_0 = const()[name = tensor<string, []>("op_511_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_518 = const()[name = tensor<string, []>("op_518"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_519_cast = reshape(shape = var_518, x = tensor_29_cast)[name = tensor<string, []>("op_519_cast")];
            tensor<int32, [4]> var_520_perm_0 = const()[name = tensor<string, []>("op_520_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_522 = const()[name = tensor<string, []>("op_522"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_139 = transpose(perm = var_520_perm_0, x = var_519_cast)[name = tensor<string, []>("transpose_139")];
            tensor<fp16, [20, 77, 64]> query_states_9_cast = reshape(shape = var_522, x = transpose_139)[name = tensor<string, []>("query_states_9_cast")];
            tensor<int32, [3]> var_524 = const()[name = tensor<string, []>("op_524"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_138 = transpose(perm = var_504_perm_0, x = var_503_cast)[name = tensor<string, []>("transpose_138")];
            tensor<fp16, [20, 77, 64]> key_states_19_cast = reshape(shape = var_524, x = transpose_138)[name = tensor<string, []>("key_states_19_cast")];
            tensor<int32, [3]> var_526 = const()[name = tensor<string, []>("op_526"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_137 = transpose(perm = var_511_perm_0, x = var_510_cast)[name = tensor<string, []>("transpose_137")];
            tensor<fp16, [20, 77, 64]> value_states_19_cast = reshape(shape = var_526, x = transpose_137)[name = tensor<string, []>("value_states_19_cast")];
            tensor<int32, [3]> var_529_perm_0 = const()[name = tensor<string, []>("op_529_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_25_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_25_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_136 = transpose(perm = var_529_perm_0, x = key_states_19_cast)[name = tensor<string, []>("transpose_136")];
            tensor<fp16, [20, 77, 77]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast, y = transpose_136)[name = tensor<string, []>("attn_weights_25_cast")];
            tensor<int32, [4]> var_531 = const()[name = tensor<string, []>("op_531"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_532_cast = reshape(shape = var_531, x = attn_weights_25_cast)[name = tensor<string, []>("op_532_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_27_cast = add(x = var_532_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_27_cast")];
            tensor<int32, [3]> var_537 = const()[name = tensor<string, []>("op_537"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_69_cast = reshape(shape = var_537, x = attn_weights_27_cast)[name = tensor<string, []>("input_69_cast")];
            tensor<fp16, [20, 77, 77]> input_71_cast = softmax(axis = var_5, x = input_69_cast)[name = tensor<string, []>("input_71_cast")];
            tensor<bool, []> attn_output_25_transpose_x_0 = const()[name = tensor<string, []>("attn_output_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_25_transpose_y_0 = const()[name = tensor<string, []>("attn_output_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_25_cast = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast, y = value_states_19_cast)[name = tensor<string, []>("attn_output_25_cast")];
            tensor<int32, [4]> var_542 = const()[name = tensor<string, []>("op_542"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_27_cast = reshape(shape = var_542, x = attn_output_25_cast)[name = tensor<string, []>("attn_output_27_cast")];
            tensor<int32, [4]> attn_output_29_perm_0 = const()[name = tensor<string, []>("attn_output_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_545 = const()[name = tensor<string, []>("op_545"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_135 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast)[name = tensor<string, []>("transpose_135")];
            tensor<fp16, [1, 77, 1280]> input_73_cast = reshape(shape = var_545, x = transpose_135)[name = tensor<string, []>("input_73_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210296704))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211935168))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211935744)))];
            tensor<fp16, [1, 77, 1280]> linear_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized, x = input_73_cast)[name = tensor<string, []>("linear_27_cast")];
            tensor<fp16, [1, 77, 1280]> input_75_cast = add(x = input_67_cast, y = linear_27_cast)[name = tensor<string, []>("input_75_cast")];
            tensor<int32, [1]> input_77_axes_0 = const()[name = tensor<string, []>("input_77_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211938368)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211940992)))];
            tensor<fp16, [1, 77, 1280]> input_77_cast = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast)[name = tensor<string, []>("input_77_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211943616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218497280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218497856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218503040))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_28_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_77_cast)[name = tensor<string, []>("linear_28_cast")];
            tensor<string, []> input_81_mode_0 = const()[name = tensor<string, []>("input_81_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_81_cast = gelu(mode = input_81_mode_0, x = linear_28_cast)[name = tensor<string, []>("input_81_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218503616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225057280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225057856)))];
            tensor<fp16, [1, 77, 1280]> linear_29_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized, x = input_81_cast)[name = tensor<string, []>("linear_29_cast")];
            tensor<fp16, [1, 77, 1280]> input_83_cast = add(x = input_75_cast, y = linear_29_cast)[name = tensor<string, []>("input_83_cast")];
            tensor<int32, [1]> hidden_states_31_axes_0 = const()[name = tensor<string, []>("hidden_states_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225060480)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225063104)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_31_cast = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast)[name = tensor<string, []>("hidden_states_31_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225065728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226704192))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226704768)))];
            tensor<fp16, [1, 77, 1280]> linear_30_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_30_cast")];
            tensor<fp16, []> var_584_to_fp16 = const()[name = tensor<string, []>("op_584_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_35_cast = mul(x = linear_30_cast, y = var_584_to_fp16)[name = tensor<string, []>("tensor_35_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226707392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228345856))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228346432)))];
            tensor<fp16, [1, 77, 1280]> linear_31_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_31_cast")];
            tensor<int32, [4]> var_589 = const()[name = tensor<string, []>("op_589"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_590_cast = reshape(shape = var_589, x = linear_31_cast)[name = tensor<string, []>("op_590_cast")];
            tensor<int32, [4]> var_591_perm_0 = const()[name = tensor<string, []>("op_591_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228349056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(229987520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(229988096)))];
            tensor<fp16, [1, 77, 1280]> linear_32_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_32_cast")];
            tensor<int32, [4]> var_596 = const()[name = tensor<string, []>("op_596"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_597_cast = reshape(shape = var_596, x = linear_32_cast)[name = tensor<string, []>("op_597_cast")];
            tensor<int32, [4]> var_598_perm_0 = const()[name = tensor<string, []>("op_598_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_605 = const()[name = tensor<string, []>("op_605"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_606_cast = reshape(shape = var_605, x = tensor_35_cast)[name = tensor<string, []>("op_606_cast")];
            tensor<int32, [4]> var_607_perm_0 = const()[name = tensor<string, []>("op_607_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_609 = const()[name = tensor<string, []>("op_609"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_134 = transpose(perm = var_607_perm_0, x = var_606_cast)[name = tensor<string, []>("transpose_134")];
            tensor<fp16, [20, 77, 64]> query_states_11_cast = reshape(shape = var_609, x = transpose_134)[name = tensor<string, []>("query_states_11_cast")];
            tensor<int32, [3]> var_611 = const()[name = tensor<string, []>("op_611"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_133 = transpose(perm = var_591_perm_0, x = var_590_cast)[name = tensor<string, []>("transpose_133")];
            tensor<fp16, [20, 77, 64]> key_states_23_cast = reshape(shape = var_611, x = transpose_133)[name = tensor<string, []>("key_states_23_cast")];
            tensor<int32, [3]> var_613 = const()[name = tensor<string, []>("op_613"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_132 = transpose(perm = var_598_perm_0, x = var_597_cast)[name = tensor<string, []>("transpose_132")];
            tensor<fp16, [20, 77, 64]> value_states_23_cast = reshape(shape = var_613, x = transpose_132)[name = tensor<string, []>("value_states_23_cast")];
            tensor<int32, [3]> var_616_perm_0 = const()[name = tensor<string, []>("op_616_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_31_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_31_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_131 = transpose(perm = var_616_perm_0, x = key_states_23_cast)[name = tensor<string, []>("transpose_131")];
            tensor<fp16, [20, 77, 77]> attn_weights_31_cast = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast, y = transpose_131)[name = tensor<string, []>("attn_weights_31_cast")];
            tensor<int32, [4]> var_618 = const()[name = tensor<string, []>("op_618"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_619_cast = reshape(shape = var_618, x = attn_weights_31_cast)[name = tensor<string, []>("op_619_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_33_cast = add(x = var_619_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_33_cast")];
            tensor<int32, [3]> var_624 = const()[name = tensor<string, []>("op_624"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_85_cast = reshape(shape = var_624, x = attn_weights_33_cast)[name = tensor<string, []>("input_85_cast")];
            tensor<fp16, [20, 77, 77]> input_87_cast = softmax(axis = var_5, x = input_85_cast)[name = tensor<string, []>("input_87_cast")];
            tensor<bool, []> attn_output_31_transpose_x_0 = const()[name = tensor<string, []>("attn_output_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_31_transpose_y_0 = const()[name = tensor<string, []>("attn_output_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_31_cast = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast, y = value_states_23_cast)[name = tensor<string, []>("attn_output_31_cast")];
            tensor<int32, [4]> var_629 = const()[name = tensor<string, []>("op_629"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_33_cast = reshape(shape = var_629, x = attn_output_31_cast)[name = tensor<string, []>("attn_output_33_cast")];
            tensor<int32, [4]> attn_output_35_perm_0 = const()[name = tensor<string, []>("attn_output_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_632 = const()[name = tensor<string, []>("op_632"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_130 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast)[name = tensor<string, []>("transpose_130")];
            tensor<fp16, [1, 77, 1280]> input_89_cast = reshape(shape = var_632, x = transpose_130)[name = tensor<string, []>("input_89_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(229990720))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231629184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231629760)))];
            tensor<fp16, [1, 77, 1280]> linear_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized, x = input_89_cast)[name = tensor<string, []>("linear_33_cast")];
            tensor<fp16, [1, 77, 1280]> input_91_cast = add(x = input_83_cast, y = linear_33_cast)[name = tensor<string, []>("input_91_cast")];
            tensor<int32, [1]> input_93_axes_0 = const()[name = tensor<string, []>("input_93_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231632384)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231635008)))];
            tensor<fp16, [1, 77, 1280]> input_93_cast = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast)[name = tensor<string, []>("input_93_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231637632))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238191296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238191872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238197056))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_34_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor<string, []>("linear_34_cast")];
            tensor<string, []> input_97_mode_0 = const()[name = tensor<string, []>("input_97_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_97_cast = gelu(mode = input_97_mode_0, x = linear_34_cast)[name = tensor<string, []>("input_97_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238197632))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244751296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244751872)))];
            tensor<fp16, [1, 77, 1280]> linear_35_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized, x = input_97_cast)[name = tensor<string, []>("linear_35_cast")];
            tensor<fp16, [1, 77, 1280]> input_99_cast = add(x = input_91_cast, y = linear_35_cast)[name = tensor<string, []>("input_99_cast")];
            tensor<int32, [1]> hidden_states_37_axes_0 = const()[name = tensor<string, []>("hidden_states_37_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244754496)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244757120)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_37_cast = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast)[name = tensor<string, []>("hidden_states_37_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244759744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246398208))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246398784)))];
            tensor<fp16, [1, 77, 1280]> linear_36_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_36_cast")];
            tensor<fp16, []> var_671_to_fp16 = const()[name = tensor<string, []>("op_671_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_41_cast = mul(x = linear_36_cast, y = var_671_to_fp16)[name = tensor<string, []>("tensor_41_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246401408))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248039872))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248040448)))];
            tensor<fp16, [1, 77, 1280]> linear_37_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_37_cast")];
            tensor<int32, [4]> var_676 = const()[name = tensor<string, []>("op_676"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_677_cast = reshape(shape = var_676, x = linear_37_cast)[name = tensor<string, []>("op_677_cast")];
            tensor<int32, [4]> var_678_perm_0 = const()[name = tensor<string, []>("op_678_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248043072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249681536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249682112)))];
            tensor<fp16, [1, 77, 1280]> linear_38_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_38_cast")];
            tensor<int32, [4]> var_683 = const()[name = tensor<string, []>("op_683"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_684_cast = reshape(shape = var_683, x = linear_38_cast)[name = tensor<string, []>("op_684_cast")];
            tensor<int32, [4]> var_685_perm_0 = const()[name = tensor<string, []>("op_685_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_692 = const()[name = tensor<string, []>("op_692"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_693_cast = reshape(shape = var_692, x = tensor_41_cast)[name = tensor<string, []>("op_693_cast")];
            tensor<int32, [4]> var_694_perm_0 = const()[name = tensor<string, []>("op_694_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_696 = const()[name = tensor<string, []>("op_696"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_129 = transpose(perm = var_694_perm_0, x = var_693_cast)[name = tensor<string, []>("transpose_129")];
            tensor<fp16, [20, 77, 64]> query_states_13_cast = reshape(shape = var_696, x = transpose_129)[name = tensor<string, []>("query_states_13_cast")];
            tensor<int32, [3]> var_698 = const()[name = tensor<string, []>("op_698"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_128 = transpose(perm = var_678_perm_0, x = var_677_cast)[name = tensor<string, []>("transpose_128")];
            tensor<fp16, [20, 77, 64]> key_states_27_cast = reshape(shape = var_698, x = transpose_128)[name = tensor<string, []>("key_states_27_cast")];
            tensor<int32, [3]> var_700 = const()[name = tensor<string, []>("op_700"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_127 = transpose(perm = var_685_perm_0, x = var_684_cast)[name = tensor<string, []>("transpose_127")];
            tensor<fp16, [20, 77, 64]> value_states_27_cast = reshape(shape = var_700, x = transpose_127)[name = tensor<string, []>("value_states_27_cast")];
            tensor<int32, [3]> var_703_perm_0 = const()[name = tensor<string, []>("op_703_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_37_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_37_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_126 = transpose(perm = var_703_perm_0, x = key_states_27_cast)[name = tensor<string, []>("transpose_126")];
            tensor<fp16, [20, 77, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast, y = transpose_126)[name = tensor<string, []>("attn_weights_37_cast")];
            tensor<int32, [4]> var_705 = const()[name = tensor<string, []>("op_705"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_706_cast = reshape(shape = var_705, x = attn_weights_37_cast)[name = tensor<string, []>("op_706_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_39_cast = add(x = var_706_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_39_cast")];
            tensor<int32, [3]> var_711 = const()[name = tensor<string, []>("op_711"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_101_cast = reshape(shape = var_711, x = attn_weights_39_cast)[name = tensor<string, []>("input_101_cast")];
            tensor<fp16, [20, 77, 77]> input_103_cast = softmax(axis = var_5, x = input_101_cast)[name = tensor<string, []>("input_103_cast")];
            tensor<bool, []> attn_output_37_transpose_x_0 = const()[name = tensor<string, []>("attn_output_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_37_transpose_y_0 = const()[name = tensor<string, []>("attn_output_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_37_cast = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast, y = value_states_27_cast)[name = tensor<string, []>("attn_output_37_cast")];
            tensor<int32, [4]> var_716 = const()[name = tensor<string, []>("op_716"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_39_cast = reshape(shape = var_716, x = attn_output_37_cast)[name = tensor<string, []>("attn_output_39_cast")];
            tensor<int32, [4]> attn_output_41_perm_0 = const()[name = tensor<string, []>("attn_output_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_719 = const()[name = tensor<string, []>("op_719"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_125 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast)[name = tensor<string, []>("transpose_125")];
            tensor<fp16, [1, 77, 1280]> input_105_cast = reshape(shape = var_719, x = transpose_125)[name = tensor<string, []>("input_105_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249684736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251323200))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251323776)))];
            tensor<fp16, [1, 77, 1280]> linear_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized, x = input_105_cast)[name = tensor<string, []>("linear_39_cast")];
            tensor<fp16, [1, 77, 1280]> input_107_cast = add(x = input_99_cast, y = linear_39_cast)[name = tensor<string, []>("input_107_cast")];
            tensor<int32, [1]> input_109_axes_0 = const()[name = tensor<string, []>("input_109_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251326400)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251329024)))];
            tensor<fp16, [1, 77, 1280]> input_109_cast = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast)[name = tensor<string, []>("input_109_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251331648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257885312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257885888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257891072))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_40_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_109_cast)[name = tensor<string, []>("linear_40_cast")];
            tensor<string, []> input_113_mode_0 = const()[name = tensor<string, []>("input_113_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_113_cast = gelu(mode = input_113_mode_0, x = linear_40_cast)[name = tensor<string, []>("input_113_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257891648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264445312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264445888)))];
            tensor<fp16, [1, 77, 1280]> linear_41_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized, x = input_113_cast)[name = tensor<string, []>("linear_41_cast")];
            tensor<fp16, [1, 77, 1280]> input_115_cast = add(x = input_107_cast, y = linear_41_cast)[name = tensor<string, []>("input_115_cast")];
            tensor<int32, [1]> hidden_states_43_axes_0 = const()[name = tensor<string, []>("hidden_states_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264448512)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264451136)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_43_cast = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast)[name = tensor<string, []>("hidden_states_43_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264453760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266092224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266092800)))];
            tensor<fp16, [1, 77, 1280]> linear_42_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_42_cast")];
            tensor<fp16, []> var_758_to_fp16 = const()[name = tensor<string, []>("op_758_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_47_cast = mul(x = linear_42_cast, y = var_758_to_fp16)[name = tensor<string, []>("tensor_47_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266095424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267733888))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267734464)))];
            tensor<fp16, [1, 77, 1280]> linear_43_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_43_cast")];
            tensor<int32, [4]> var_763 = const()[name = tensor<string, []>("op_763"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_764_cast = reshape(shape = var_763, x = linear_43_cast)[name = tensor<string, []>("op_764_cast")];
            tensor<int32, [4]> var_765_perm_0 = const()[name = tensor<string, []>("op_765_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267737088))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269375552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269376128)))];
            tensor<fp16, [1, 77, 1280]> linear_44_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_44_cast")];
            tensor<int32, [4]> var_770 = const()[name = tensor<string, []>("op_770"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_771_cast = reshape(shape = var_770, x = linear_44_cast)[name = tensor<string, []>("op_771_cast")];
            tensor<int32, [4]> var_772_perm_0 = const()[name = tensor<string, []>("op_772_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_780_cast = reshape(shape = var_779, x = tensor_47_cast)[name = tensor<string, []>("op_780_cast")];
            tensor<int32, [4]> var_781_perm_0 = const()[name = tensor<string, []>("op_781_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_783 = const()[name = tensor<string, []>("op_783"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_124 = transpose(perm = var_781_perm_0, x = var_780_cast)[name = tensor<string, []>("transpose_124")];
            tensor<fp16, [20, 77, 64]> query_states_15_cast = reshape(shape = var_783, x = transpose_124)[name = tensor<string, []>("query_states_15_cast")];
            tensor<int32, [3]> var_785 = const()[name = tensor<string, []>("op_785"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_123 = transpose(perm = var_765_perm_0, x = var_764_cast)[name = tensor<string, []>("transpose_123")];
            tensor<fp16, [20, 77, 64]> key_states_31_cast = reshape(shape = var_785, x = transpose_123)[name = tensor<string, []>("key_states_31_cast")];
            tensor<int32, [3]> var_787 = const()[name = tensor<string, []>("op_787"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_122 = transpose(perm = var_772_perm_0, x = var_771_cast)[name = tensor<string, []>("transpose_122")];
            tensor<fp16, [20, 77, 64]> value_states_31_cast = reshape(shape = var_787, x = transpose_122)[name = tensor<string, []>("value_states_31_cast")];
            tensor<int32, [3]> var_790_perm_0 = const()[name = tensor<string, []>("op_790_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_43_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_43_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_121 = transpose(perm = var_790_perm_0, x = key_states_31_cast)[name = tensor<string, []>("transpose_121")];
            tensor<fp16, [20, 77, 77]> attn_weights_43_cast = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast, y = transpose_121)[name = tensor<string, []>("attn_weights_43_cast")];
            tensor<int32, [4]> var_792 = const()[name = tensor<string, []>("op_792"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_793_cast = reshape(shape = var_792, x = attn_weights_43_cast)[name = tensor<string, []>("op_793_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_45_cast = add(x = var_793_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_45_cast")];
            tensor<int32, [3]> var_798 = const()[name = tensor<string, []>("op_798"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_117_cast = reshape(shape = var_798, x = attn_weights_45_cast)[name = tensor<string, []>("input_117_cast")];
            tensor<fp16, [20, 77, 77]> input_119_cast = softmax(axis = var_5, x = input_117_cast)[name = tensor<string, []>("input_119_cast")];
            tensor<bool, []> attn_output_43_transpose_x_0 = const()[name = tensor<string, []>("attn_output_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_43_transpose_y_0 = const()[name = tensor<string, []>("attn_output_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_43_cast = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast, y = value_states_31_cast)[name = tensor<string, []>("attn_output_43_cast")];
            tensor<int32, [4]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_45_cast = reshape(shape = var_803, x = attn_output_43_cast)[name = tensor<string, []>("attn_output_45_cast")];
            tensor<int32, [4]> attn_output_47_perm_0 = const()[name = tensor<string, []>("attn_output_47_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_806 = const()[name = tensor<string, []>("op_806"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_120 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast)[name = tensor<string, []>("transpose_120")];
            tensor<fp16, [1, 77, 1280]> input_121_cast = reshape(shape = var_806, x = transpose_120)[name = tensor<string, []>("input_121_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269378752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271017216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271017792)))];
            tensor<fp16, [1, 77, 1280]> linear_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized, x = input_121_cast)[name = tensor<string, []>("linear_45_cast")];
            tensor<fp16, [1, 77, 1280]> input_123_cast = add(x = input_115_cast, y = linear_45_cast)[name = tensor<string, []>("input_123_cast")];
            tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271020416)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271023040)))];
            tensor<fp16, [1, 77, 1280]> input_125_cast = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast)[name = tensor<string, []>("input_125_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271025664))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277579328))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277579904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277585088))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_46_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_125_cast)[name = tensor<string, []>("linear_46_cast")];
            tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_129_cast = gelu(mode = input_129_mode_0, x = linear_46_cast)[name = tensor<string, []>("input_129_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277585664))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284139328))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284139904)))];
            tensor<fp16, [1, 77, 1280]> linear_47_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor<string, []>("linear_47_cast")];
            tensor<fp16, [1, 77, 1280]> input_131_cast = add(x = input_123_cast, y = linear_47_cast)[name = tensor<string, []>("input_131_cast")];
            tensor<int32, [1]> hidden_states_49_axes_0 = const()[name = tensor<string, []>("hidden_states_49_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284142528)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284145152)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_49_cast = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast)[name = tensor<string, []>("hidden_states_49_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284147776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285786240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285786816)))];
            tensor<fp16, [1, 77, 1280]> linear_48_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_48_cast")];
            tensor<fp16, []> var_845_to_fp16 = const()[name = tensor<string, []>("op_845_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_53_cast = mul(x = linear_48_cast, y = var_845_to_fp16)[name = tensor<string, []>("tensor_53_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285789440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287427904))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287428480)))];
            tensor<fp16, [1, 77, 1280]> linear_49_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_49_cast")];
            tensor<int32, [4]> var_850 = const()[name = tensor<string, []>("op_850"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_851_cast = reshape(shape = var_850, x = linear_49_cast)[name = tensor<string, []>("op_851_cast")];
            tensor<int32, [4]> var_852_perm_0 = const()[name = tensor<string, []>("op_852_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287431104))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289069568))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289070144)))];
            tensor<fp16, [1, 77, 1280]> linear_50_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_50_cast")];
            tensor<int32, [4]> var_857 = const()[name = tensor<string, []>("op_857"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_858_cast = reshape(shape = var_857, x = linear_50_cast)[name = tensor<string, []>("op_858_cast")];
            tensor<int32, [4]> var_859_perm_0 = const()[name = tensor<string, []>("op_859_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_866 = const()[name = tensor<string, []>("op_866"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_867_cast = reshape(shape = var_866, x = tensor_53_cast)[name = tensor<string, []>("op_867_cast")];
            tensor<int32, [4]> var_868_perm_0 = const()[name = tensor<string, []>("op_868_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_870 = const()[name = tensor<string, []>("op_870"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_119 = transpose(perm = var_868_perm_0, x = var_867_cast)[name = tensor<string, []>("transpose_119")];
            tensor<fp16, [20, 77, 64]> query_states_17_cast = reshape(shape = var_870, x = transpose_119)[name = tensor<string, []>("query_states_17_cast")];
            tensor<int32, [3]> var_872 = const()[name = tensor<string, []>("op_872"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_118 = transpose(perm = var_852_perm_0, x = var_851_cast)[name = tensor<string, []>("transpose_118")];
            tensor<fp16, [20, 77, 64]> key_states_35_cast = reshape(shape = var_872, x = transpose_118)[name = tensor<string, []>("key_states_35_cast")];
            tensor<int32, [3]> var_874 = const()[name = tensor<string, []>("op_874"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_117 = transpose(perm = var_859_perm_0, x = var_858_cast)[name = tensor<string, []>("transpose_117")];
            tensor<fp16, [20, 77, 64]> value_states_35_cast = reshape(shape = var_874, x = transpose_117)[name = tensor<string, []>("value_states_35_cast")];
            tensor<int32, [3]> var_877_perm_0 = const()[name = tensor<string, []>("op_877_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_49_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_49_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_116 = transpose(perm = var_877_perm_0, x = key_states_35_cast)[name = tensor<string, []>("transpose_116")];
            tensor<fp16, [20, 77, 77]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast, y = transpose_116)[name = tensor<string, []>("attn_weights_49_cast")];
            tensor<int32, [4]> var_879 = const()[name = tensor<string, []>("op_879"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_880_cast = reshape(shape = var_879, x = attn_weights_49_cast)[name = tensor<string, []>("op_880_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_51_cast = add(x = var_880_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_51_cast")];
            tensor<int32, [3]> var_885 = const()[name = tensor<string, []>("op_885"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_133_cast = reshape(shape = var_885, x = attn_weights_51_cast)[name = tensor<string, []>("input_133_cast")];
            tensor<fp16, [20, 77, 77]> input_135_cast = softmax(axis = var_5, x = input_133_cast)[name = tensor<string, []>("input_135_cast")];
            tensor<bool, []> attn_output_49_transpose_x_0 = const()[name = tensor<string, []>("attn_output_49_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_49_transpose_y_0 = const()[name = tensor<string, []>("attn_output_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_49_cast = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast, y = value_states_35_cast)[name = tensor<string, []>("attn_output_49_cast")];
            tensor<int32, [4]> var_890 = const()[name = tensor<string, []>("op_890"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_51_cast = reshape(shape = var_890, x = attn_output_49_cast)[name = tensor<string, []>("attn_output_51_cast")];
            tensor<int32, [4]> attn_output_53_perm_0 = const()[name = tensor<string, []>("attn_output_53_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_893 = const()[name = tensor<string, []>("op_893"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_115 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast)[name = tensor<string, []>("transpose_115")];
            tensor<fp16, [1, 77, 1280]> input_137_cast = reshape(shape = var_893, x = transpose_115)[name = tensor<string, []>("input_137_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289072768))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290711232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290711808)))];
            tensor<fp16, [1, 77, 1280]> linear_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor<string, []>("linear_51_cast")];
            tensor<fp16, [1, 77, 1280]> input_139_cast = add(x = input_131_cast, y = linear_51_cast)[name = tensor<string, []>("input_139_cast")];
            tensor<int32, [1]> input_141_axes_0 = const()[name = tensor<string, []>("input_141_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290714432)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290717056)))];
            tensor<fp16, [1, 77, 1280]> input_141_cast = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast)[name = tensor<string, []>("input_141_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290719680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297273344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297273920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297279104))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_52_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized, x = input_141_cast)[name = tensor<string, []>("linear_52_cast")];
            tensor<string, []> input_145_mode_0 = const()[name = tensor<string, []>("input_145_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_145_cast = gelu(mode = input_145_mode_0, x = linear_52_cast)[name = tensor<string, []>("input_145_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297279680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303833344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303833920)))];
            tensor<fp16, [1, 77, 1280]> linear_53_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized, x = input_145_cast)[name = tensor<string, []>("linear_53_cast")];
            tensor<fp16, [1, 77, 1280]> input_147_cast = add(x = input_139_cast, y = linear_53_cast)[name = tensor<string, []>("input_147_cast")];
            tensor<int32, [1]> hidden_states_55_axes_0 = const()[name = tensor<string, []>("hidden_states_55_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303836544)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303839168)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_55_cast = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast)[name = tensor<string, []>("hidden_states_55_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303841792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305480256))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305480832)))];
            tensor<fp16, [1, 77, 1280]> linear_54_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_54_cast")];
            tensor<fp16, []> var_932_to_fp16 = const()[name = tensor<string, []>("op_932_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_59_cast = mul(x = linear_54_cast, y = var_932_to_fp16)[name = tensor<string, []>("tensor_59_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305483456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307121920))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307122496)))];
            tensor<fp16, [1, 77, 1280]> linear_55_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_55_cast")];
            tensor<int32, [4]> var_937 = const()[name = tensor<string, []>("op_937"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_938_cast = reshape(shape = var_937, x = linear_55_cast)[name = tensor<string, []>("op_938_cast")];
            tensor<int32, [4]> var_939_perm_0 = const()[name = tensor<string, []>("op_939_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307125120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308763584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308764160)))];
            tensor<fp16, [1, 77, 1280]> linear_56_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_56_cast")];
            tensor<int32, [4]> var_944 = const()[name = tensor<string, []>("op_944"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_945_cast = reshape(shape = var_944, x = linear_56_cast)[name = tensor<string, []>("op_945_cast")];
            tensor<int32, [4]> var_946_perm_0 = const()[name = tensor<string, []>("op_946_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_954_cast = reshape(shape = var_953, x = tensor_59_cast)[name = tensor<string, []>("op_954_cast")];
            tensor<int32, [4]> var_955_perm_0 = const()[name = tensor<string, []>("op_955_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_957 = const()[name = tensor<string, []>("op_957"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_114 = transpose(perm = var_955_perm_0, x = var_954_cast)[name = tensor<string, []>("transpose_114")];
            tensor<fp16, [20, 77, 64]> query_states_19_cast = reshape(shape = var_957, x = transpose_114)[name = tensor<string, []>("query_states_19_cast")];
            tensor<int32, [3]> var_959 = const()[name = tensor<string, []>("op_959"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_113 = transpose(perm = var_939_perm_0, x = var_938_cast)[name = tensor<string, []>("transpose_113")];
            tensor<fp16, [20, 77, 64]> key_states_39_cast = reshape(shape = var_959, x = transpose_113)[name = tensor<string, []>("key_states_39_cast")];
            tensor<int32, [3]> var_961 = const()[name = tensor<string, []>("op_961"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_112 = transpose(perm = var_946_perm_0, x = var_945_cast)[name = tensor<string, []>("transpose_112")];
            tensor<fp16, [20, 77, 64]> value_states_39_cast = reshape(shape = var_961, x = transpose_112)[name = tensor<string, []>("value_states_39_cast")];
            tensor<int32, [3]> var_964_perm_0 = const()[name = tensor<string, []>("op_964_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_55_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_55_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_111 = transpose(perm = var_964_perm_0, x = key_states_39_cast)[name = tensor<string, []>("transpose_111")];
            tensor<fp16, [20, 77, 77]> attn_weights_55_cast = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast, y = transpose_111)[name = tensor<string, []>("attn_weights_55_cast")];
            tensor<int32, [4]> var_966 = const()[name = tensor<string, []>("op_966"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_967_cast = reshape(shape = var_966, x = attn_weights_55_cast)[name = tensor<string, []>("op_967_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_57_cast = add(x = var_967_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_57_cast")];
            tensor<int32, [3]> var_972 = const()[name = tensor<string, []>("op_972"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_149_cast = reshape(shape = var_972, x = attn_weights_57_cast)[name = tensor<string, []>("input_149_cast")];
            tensor<fp16, [20, 77, 77]> input_151_cast = softmax(axis = var_5, x = input_149_cast)[name = tensor<string, []>("input_151_cast")];
            tensor<bool, []> attn_output_55_transpose_x_0 = const()[name = tensor<string, []>("attn_output_55_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_55_transpose_y_0 = const()[name = tensor<string, []>("attn_output_55_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_55_cast = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast, y = value_states_39_cast)[name = tensor<string, []>("attn_output_55_cast")];
            tensor<int32, [4]> var_977 = const()[name = tensor<string, []>("op_977"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_57_cast = reshape(shape = var_977, x = attn_output_55_cast)[name = tensor<string, []>("attn_output_57_cast")];
            tensor<int32, [4]> attn_output_59_perm_0 = const()[name = tensor<string, []>("attn_output_59_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_980 = const()[name = tensor<string, []>("op_980"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_110 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast)[name = tensor<string, []>("transpose_110")];
            tensor<fp16, [1, 77, 1280]> input_153_cast = reshape(shape = var_980, x = transpose_110)[name = tensor<string, []>("input_153_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308766784))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310405248))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310405824)))];
            tensor<fp16, [1, 77, 1280]> linear_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized, x = input_153_cast)[name = tensor<string, []>("linear_57_cast")];
            tensor<fp16, [1, 77, 1280]> input_155_cast = add(x = input_147_cast, y = linear_57_cast)[name = tensor<string, []>("input_155_cast")];
            tensor<int32, [1]> input_157_axes_0 = const()[name = tensor<string, []>("input_157_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310408448)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310411072)))];
            tensor<fp16, [1, 77, 1280]> input_157_cast = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast)[name = tensor<string, []>("input_157_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310413696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316967360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316967936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316973120))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_58_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized, x = input_157_cast)[name = tensor<string, []>("linear_58_cast")];
            tensor<string, []> input_161_mode_0 = const()[name = tensor<string, []>("input_161_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_161_cast = gelu(mode = input_161_mode_0, x = linear_58_cast)[name = tensor<string, []>("input_161_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316973696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323527360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323527936)))];
            tensor<fp16, [1, 77, 1280]> linear_59_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized, x = input_161_cast)[name = tensor<string, []>("linear_59_cast")];
            tensor<fp16, [1, 77, 1280]> input_163_cast = add(x = input_155_cast, y = linear_59_cast)[name = tensor<string, []>("input_163_cast")];
            tensor<int32, [1]> hidden_states_61_axes_0 = const()[name = tensor<string, []>("hidden_states_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323530560)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323533184)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_61_cast = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast)[name = tensor<string, []>("hidden_states_61_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323535808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325174272))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325174848)))];
            tensor<fp16, [1, 77, 1280]> linear_60_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_60_cast")];
            tensor<fp16, []> var_1019_to_fp16 = const()[name = tensor<string, []>("op_1019_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_65_cast = mul(x = linear_60_cast, y = var_1019_to_fp16)[name = tensor<string, []>("tensor_65_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325177472))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326815936))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326816512)))];
            tensor<fp16, [1, 77, 1280]> linear_61_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_61_cast")];
            tensor<int32, [4]> var_1024 = const()[name = tensor<string, []>("op_1024"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1025_cast = reshape(shape = var_1024, x = linear_61_cast)[name = tensor<string, []>("op_1025_cast")];
            tensor<int32, [4]> var_1026_perm_0 = const()[name = tensor<string, []>("op_1026_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326819136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328457600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328458176)))];
            tensor<fp16, [1, 77, 1280]> linear_62_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_62_cast")];
            tensor<int32, [4]> var_1031 = const()[name = tensor<string, []>("op_1031"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1032_cast = reshape(shape = var_1031, x = linear_62_cast)[name = tensor<string, []>("op_1032_cast")];
            tensor<int32, [4]> var_1033_perm_0 = const()[name = tensor<string, []>("op_1033_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1040 = const()[name = tensor<string, []>("op_1040"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1041_cast = reshape(shape = var_1040, x = tensor_65_cast)[name = tensor<string, []>("op_1041_cast")];
            tensor<int32, [4]> var_1042_perm_0 = const()[name = tensor<string, []>("op_1042_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1044 = const()[name = tensor<string, []>("op_1044"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_109 = transpose(perm = var_1042_perm_0, x = var_1041_cast)[name = tensor<string, []>("transpose_109")];
            tensor<fp16, [20, 77, 64]> query_states_21_cast = reshape(shape = var_1044, x = transpose_109)[name = tensor<string, []>("query_states_21_cast")];
            tensor<int32, [3]> var_1046 = const()[name = tensor<string, []>("op_1046"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_108 = transpose(perm = var_1026_perm_0, x = var_1025_cast)[name = tensor<string, []>("transpose_108")];
            tensor<fp16, [20, 77, 64]> key_states_43_cast = reshape(shape = var_1046, x = transpose_108)[name = tensor<string, []>("key_states_43_cast")];
            tensor<int32, [3]> var_1048 = const()[name = tensor<string, []>("op_1048"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_107 = transpose(perm = var_1033_perm_0, x = var_1032_cast)[name = tensor<string, []>("transpose_107")];
            tensor<fp16, [20, 77, 64]> value_states_43_cast = reshape(shape = var_1048, x = transpose_107)[name = tensor<string, []>("value_states_43_cast")];
            tensor<int32, [3]> var_1051_perm_0 = const()[name = tensor<string, []>("op_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_61_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_61_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_106 = transpose(perm = var_1051_perm_0, x = key_states_43_cast)[name = tensor<string, []>("transpose_106")];
            tensor<fp16, [20, 77, 77]> attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast, y = transpose_106)[name = tensor<string, []>("attn_weights_61_cast")];
            tensor<int32, [4]> var_1053 = const()[name = tensor<string, []>("op_1053"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1054_cast = reshape(shape = var_1053, x = attn_weights_61_cast)[name = tensor<string, []>("op_1054_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_63_cast = add(x = var_1054_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_63_cast")];
            tensor<int32, [3]> var_1059 = const()[name = tensor<string, []>("op_1059"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_165_cast = reshape(shape = var_1059, x = attn_weights_63_cast)[name = tensor<string, []>("input_165_cast")];
            tensor<fp16, [20, 77, 77]> input_167_cast = softmax(axis = var_5, x = input_165_cast)[name = tensor<string, []>("input_167_cast")];
            tensor<bool, []> attn_output_61_transpose_x_0 = const()[name = tensor<string, []>("attn_output_61_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_61_transpose_y_0 = const()[name = tensor<string, []>("attn_output_61_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_61_cast = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast, y = value_states_43_cast)[name = tensor<string, []>("attn_output_61_cast")];
            tensor<int32, [4]> var_1064 = const()[name = tensor<string, []>("op_1064"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_63_cast = reshape(shape = var_1064, x = attn_output_61_cast)[name = tensor<string, []>("attn_output_63_cast")];
            tensor<int32, [4]> attn_output_65_perm_0 = const()[name = tensor<string, []>("attn_output_65_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1067 = const()[name = tensor<string, []>("op_1067"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_105 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast)[name = tensor<string, []>("transpose_105")];
            tensor<fp16, [1, 77, 1280]> input_169_cast = reshape(shape = var_1067, x = transpose_105)[name = tensor<string, []>("input_169_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328460800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330099264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330099840)))];
            tensor<fp16, [1, 77, 1280]> linear_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized, x = input_169_cast)[name = tensor<string, []>("linear_63_cast")];
            tensor<fp16, [1, 77, 1280]> input_171_cast = add(x = input_163_cast, y = linear_63_cast)[name = tensor<string, []>("input_171_cast")];
            tensor<int32, [1]> input_173_axes_0 = const()[name = tensor<string, []>("input_173_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330102464)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330105088)))];
            tensor<fp16, [1, 77, 1280]> input_173_cast = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast)[name = tensor<string, []>("input_173_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330107712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336661376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336661952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336667136))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_64_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized, x = input_173_cast)[name = tensor<string, []>("linear_64_cast")];
            tensor<string, []> input_177_mode_0 = const()[name = tensor<string, []>("input_177_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_177_cast = gelu(mode = input_177_mode_0, x = linear_64_cast)[name = tensor<string, []>("input_177_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336667712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343221376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343221952)))];
            tensor<fp16, [1, 77, 1280]> linear_65_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized, x = input_177_cast)[name = tensor<string, []>("linear_65_cast")];
            tensor<fp16, [1, 77, 1280]> input_179_cast = add(x = input_171_cast, y = linear_65_cast)[name = tensor<string, []>("input_179_cast")];
            tensor<int32, [1]> hidden_states_67_axes_0 = const()[name = tensor<string, []>("hidden_states_67_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343224576)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343227200)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_67_cast = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast)[name = tensor<string, []>("hidden_states_67_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343229824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344868288))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344868864)))];
            tensor<fp16, [1, 77, 1280]> linear_66_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_66_cast")];
            tensor<fp16, []> var_1106_to_fp16 = const()[name = tensor<string, []>("op_1106_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_71_cast = mul(x = linear_66_cast, y = var_1106_to_fp16)[name = tensor<string, []>("tensor_71_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344871488))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346509952))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346510528)))];
            tensor<fp16, [1, 77, 1280]> linear_67_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_67_cast")];
            tensor<int32, [4]> var_1111 = const()[name = tensor<string, []>("op_1111"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1112_cast = reshape(shape = var_1111, x = linear_67_cast)[name = tensor<string, []>("op_1112_cast")];
            tensor<int32, [4]> var_1113_perm_0 = const()[name = tensor<string, []>("op_1113_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346513152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348151616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348152192)))];
            tensor<fp16, [1, 77, 1280]> linear_68_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_68_cast")];
            tensor<int32, [4]> var_1118 = const()[name = tensor<string, []>("op_1118"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1119_cast = reshape(shape = var_1118, x = linear_68_cast)[name = tensor<string, []>("op_1119_cast")];
            tensor<int32, [4]> var_1120_perm_0 = const()[name = tensor<string, []>("op_1120_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1127 = const()[name = tensor<string, []>("op_1127"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1128_cast = reshape(shape = var_1127, x = tensor_71_cast)[name = tensor<string, []>("op_1128_cast")];
            tensor<int32, [4]> var_1129_perm_0 = const()[name = tensor<string, []>("op_1129_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1131 = const()[name = tensor<string, []>("op_1131"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_104 = transpose(perm = var_1129_perm_0, x = var_1128_cast)[name = tensor<string, []>("transpose_104")];
            tensor<fp16, [20, 77, 64]> query_states_23_cast = reshape(shape = var_1131, x = transpose_104)[name = tensor<string, []>("query_states_23_cast")];
            tensor<int32, [3]> var_1133 = const()[name = tensor<string, []>("op_1133"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_103 = transpose(perm = var_1113_perm_0, x = var_1112_cast)[name = tensor<string, []>("transpose_103")];
            tensor<fp16, [20, 77, 64]> key_states_47_cast = reshape(shape = var_1133, x = transpose_103)[name = tensor<string, []>("key_states_47_cast")];
            tensor<int32, [3]> var_1135 = const()[name = tensor<string, []>("op_1135"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_102 = transpose(perm = var_1120_perm_0, x = var_1119_cast)[name = tensor<string, []>("transpose_102")];
            tensor<fp16, [20, 77, 64]> value_states_47_cast = reshape(shape = var_1135, x = transpose_102)[name = tensor<string, []>("value_states_47_cast")];
            tensor<int32, [3]> var_1138_perm_0 = const()[name = tensor<string, []>("op_1138_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_67_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_67_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_101 = transpose(perm = var_1138_perm_0, x = key_states_47_cast)[name = tensor<string, []>("transpose_101")];
            tensor<fp16, [20, 77, 77]> attn_weights_67_cast = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_23_cast, y = transpose_101)[name = tensor<string, []>("attn_weights_67_cast")];
            tensor<int32, [4]> var_1140 = const()[name = tensor<string, []>("op_1140"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1141_cast = reshape(shape = var_1140, x = attn_weights_67_cast)[name = tensor<string, []>("op_1141_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_69_cast = add(x = var_1141_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_69_cast")];
            tensor<int32, [3]> var_1146 = const()[name = tensor<string, []>("op_1146"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_181_cast = reshape(shape = var_1146, x = attn_weights_69_cast)[name = tensor<string, []>("input_181_cast")];
            tensor<fp16, [20, 77, 77]> input_183_cast = softmax(axis = var_5, x = input_181_cast)[name = tensor<string, []>("input_183_cast")];
            tensor<bool, []> attn_output_67_transpose_x_0 = const()[name = tensor<string, []>("attn_output_67_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_67_transpose_y_0 = const()[name = tensor<string, []>("attn_output_67_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_67_cast = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast, y = value_states_47_cast)[name = tensor<string, []>("attn_output_67_cast")];
            tensor<int32, [4]> var_1151 = const()[name = tensor<string, []>("op_1151"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_69_cast = reshape(shape = var_1151, x = attn_output_67_cast)[name = tensor<string, []>("attn_output_69_cast")];
            tensor<int32, [4]> attn_output_71_perm_0 = const()[name = tensor<string, []>("attn_output_71_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1154 = const()[name = tensor<string, []>("op_1154"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_100 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast)[name = tensor<string, []>("transpose_100")];
            tensor<fp16, [1, 77, 1280]> input_185_cast = reshape(shape = var_1154, x = transpose_100)[name = tensor<string, []>("input_185_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348154816))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349793280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349793856)))];
            tensor<fp16, [1, 77, 1280]> linear_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized, x = input_185_cast)[name = tensor<string, []>("linear_69_cast")];
            tensor<fp16, [1, 77, 1280]> input_187_cast = add(x = input_179_cast, y = linear_69_cast)[name = tensor<string, []>("input_187_cast")];
            tensor<int32, [1]> input_189_axes_0 = const()[name = tensor<string, []>("input_189_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349796480)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349799104)))];
            tensor<fp16, [1, 77, 1280]> input_189_cast = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast)[name = tensor<string, []>("input_189_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349801728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356355392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356355968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356361152))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_70_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor<string, []>("linear_70_cast")];
            tensor<string, []> input_193_mode_0 = const()[name = tensor<string, []>("input_193_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_193_cast = gelu(mode = input_193_mode_0, x = linear_70_cast)[name = tensor<string, []>("input_193_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356361728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362915392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362915968)))];
            tensor<fp16, [1, 77, 1280]> linear_71_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized, x = input_193_cast)[name = tensor<string, []>("linear_71_cast")];
            tensor<fp16, [1, 77, 1280]> input_195_cast = add(x = input_187_cast, y = linear_71_cast)[name = tensor<string, []>("input_195_cast")];
            tensor<int32, [1]> hidden_states_73_axes_0 = const()[name = tensor<string, []>("hidden_states_73_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362918592)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362921216)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_73_cast = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_195_cast)[name = tensor<string, []>("hidden_states_73_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362923840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364562304))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364562880)))];
            tensor<fp16, [1, 77, 1280]> linear_72_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_72_cast")];
            tensor<fp16, []> var_1193_to_fp16 = const()[name = tensor<string, []>("op_1193_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_77_cast = mul(x = linear_72_cast, y = var_1193_to_fp16)[name = tensor<string, []>("tensor_77_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364565504))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366203968))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366204544)))];
            tensor<fp16, [1, 77, 1280]> linear_73_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_73_cast")];
            tensor<int32, [4]> var_1198 = const()[name = tensor<string, []>("op_1198"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1199_cast = reshape(shape = var_1198, x = linear_73_cast)[name = tensor<string, []>("op_1199_cast")];
            tensor<int32, [4]> var_1200_perm_0 = const()[name = tensor<string, []>("op_1200_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366207168))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367845632))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367846208)))];
            tensor<fp16, [1, 77, 1280]> linear_74_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_74_cast")];
            tensor<int32, [4]> var_1205 = const()[name = tensor<string, []>("op_1205"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1206_cast = reshape(shape = var_1205, x = linear_74_cast)[name = tensor<string, []>("op_1206_cast")];
            tensor<int32, [4]> var_1207_perm_0 = const()[name = tensor<string, []>("op_1207_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1214 = const()[name = tensor<string, []>("op_1214"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1215_cast = reshape(shape = var_1214, x = tensor_77_cast)[name = tensor<string, []>("op_1215_cast")];
            tensor<int32, [4]> var_1216_perm_0 = const()[name = tensor<string, []>("op_1216_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1218 = const()[name = tensor<string, []>("op_1218"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_99 = transpose(perm = var_1216_perm_0, x = var_1215_cast)[name = tensor<string, []>("transpose_99")];
            tensor<fp16, [20, 77, 64]> query_states_25_cast = reshape(shape = var_1218, x = transpose_99)[name = tensor<string, []>("query_states_25_cast")];
            tensor<int32, [3]> var_1220 = const()[name = tensor<string, []>("op_1220"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_98 = transpose(perm = var_1200_perm_0, x = var_1199_cast)[name = tensor<string, []>("transpose_98")];
            tensor<fp16, [20, 77, 64]> key_states_51_cast = reshape(shape = var_1220, x = transpose_98)[name = tensor<string, []>("key_states_51_cast")];
            tensor<int32, [3]> var_1222 = const()[name = tensor<string, []>("op_1222"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_97 = transpose(perm = var_1207_perm_0, x = var_1206_cast)[name = tensor<string, []>("transpose_97")];
            tensor<fp16, [20, 77, 64]> value_states_51_cast = reshape(shape = var_1222, x = transpose_97)[name = tensor<string, []>("value_states_51_cast")];
            tensor<int32, [3]> var_1225_perm_0 = const()[name = tensor<string, []>("op_1225_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_73_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_73_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_96 = transpose(perm = var_1225_perm_0, x = key_states_51_cast)[name = tensor<string, []>("transpose_96")];
            tensor<fp16, [20, 77, 77]> attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = query_states_25_cast, y = transpose_96)[name = tensor<string, []>("attn_weights_73_cast")];
            tensor<int32, [4]> var_1227 = const()[name = tensor<string, []>("op_1227"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1228_cast = reshape(shape = var_1227, x = attn_weights_73_cast)[name = tensor<string, []>("op_1228_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_75_cast = add(x = var_1228_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_75_cast")];
            tensor<int32, [3]> var_1233 = const()[name = tensor<string, []>("op_1233"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_197_cast = reshape(shape = var_1233, x = attn_weights_75_cast)[name = tensor<string, []>("input_197_cast")];
            tensor<fp16, [20, 77, 77]> input_199_cast = softmax(axis = var_5, x = input_197_cast)[name = tensor<string, []>("input_199_cast")];
            tensor<bool, []> attn_output_73_transpose_x_0 = const()[name = tensor<string, []>("attn_output_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_73_transpose_y_0 = const()[name = tensor<string, []>("attn_output_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_73_cast = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = input_199_cast, y = value_states_51_cast)[name = tensor<string, []>("attn_output_73_cast")];
            tensor<int32, [4]> var_1238 = const()[name = tensor<string, []>("op_1238"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_75_cast = reshape(shape = var_1238, x = attn_output_73_cast)[name = tensor<string, []>("attn_output_75_cast")];
            tensor<int32, [4]> attn_output_77_perm_0 = const()[name = tensor<string, []>("attn_output_77_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1241 = const()[name = tensor<string, []>("op_1241"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_95 = transpose(perm = attn_output_77_perm_0, x = attn_output_75_cast)[name = tensor<string, []>("transpose_95")];
            tensor<fp16, [1, 77, 1280]> input_201_cast = reshape(shape = var_1241, x = transpose_95)[name = tensor<string, []>("input_201_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367848832))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369487296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369487872)))];
            tensor<fp16, [1, 77, 1280]> linear_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized, x = input_201_cast)[name = tensor<string, []>("linear_75_cast")];
            tensor<fp16, [1, 77, 1280]> input_203_cast = add(x = input_195_cast, y = linear_75_cast)[name = tensor<string, []>("input_203_cast")];
            tensor<int32, [1]> input_205_axes_0 = const()[name = tensor<string, []>("input_205_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369490496)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369493120)))];
            tensor<fp16, [1, 77, 1280]> input_205_cast = layer_norm(axes = input_205_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_203_cast)[name = tensor<string, []>("input_205_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(369495744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376049408))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376049984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376055168))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_76_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized, x = input_205_cast)[name = tensor<string, []>("linear_76_cast")];
            tensor<string, []> input_209_mode_0 = const()[name = tensor<string, []>("input_209_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_209_cast = gelu(mode = input_209_mode_0, x = linear_76_cast)[name = tensor<string, []>("input_209_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376055744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382609408))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382609984)))];
            tensor<fp16, [1, 77, 1280]> linear_77_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized, x = input_209_cast)[name = tensor<string, []>("linear_77_cast")];
            tensor<fp16, [1, 77, 1280]> input_211_cast = add(x = input_203_cast, y = linear_77_cast)[name = tensor<string, []>("input_211_cast")];
            tensor<int32, [1]> hidden_states_79_axes_0 = const()[name = tensor<string, []>("hidden_states_79_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382612608)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382615232)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_79_cast = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_211_cast)[name = tensor<string, []>("hidden_states_79_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382617856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384256320))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384256896)))];
            tensor<fp16, [1, 77, 1280]> linear_78_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_78_cast")];
            tensor<fp16, []> var_1280_to_fp16 = const()[name = tensor<string, []>("op_1280_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_83_cast = mul(x = linear_78_cast, y = var_1280_to_fp16)[name = tensor<string, []>("tensor_83_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384259520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385897984))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385898560)))];
            tensor<fp16, [1, 77, 1280]> linear_79_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_79_cast")];
            tensor<int32, [4]> var_1285 = const()[name = tensor<string, []>("op_1285"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1286_cast = reshape(shape = var_1285, x = linear_79_cast)[name = tensor<string, []>("op_1286_cast")];
            tensor<int32, [4]> var_1287_perm_0 = const()[name = tensor<string, []>("op_1287_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385901184))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387539648))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387540224)))];
            tensor<fp16, [1, 77, 1280]> linear_80_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_80_cast")];
            tensor<int32, [4]> var_1292 = const()[name = tensor<string, []>("op_1292"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1293_cast = reshape(shape = var_1292, x = linear_80_cast)[name = tensor<string, []>("op_1293_cast")];
            tensor<int32, [4]> var_1294_perm_0 = const()[name = tensor<string, []>("op_1294_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1301 = const()[name = tensor<string, []>("op_1301"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1302_cast = reshape(shape = var_1301, x = tensor_83_cast)[name = tensor<string, []>("op_1302_cast")];
            tensor<int32, [4]> var_1303_perm_0 = const()[name = tensor<string, []>("op_1303_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1305 = const()[name = tensor<string, []>("op_1305"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_94 = transpose(perm = var_1303_perm_0, x = var_1302_cast)[name = tensor<string, []>("transpose_94")];
            tensor<fp16, [20, 77, 64]> query_states_27_cast = reshape(shape = var_1305, x = transpose_94)[name = tensor<string, []>("query_states_27_cast")];
            tensor<int32, [3]> var_1307 = const()[name = tensor<string, []>("op_1307"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_93 = transpose(perm = var_1287_perm_0, x = var_1286_cast)[name = tensor<string, []>("transpose_93")];
            tensor<fp16, [20, 77, 64]> key_states_55_cast = reshape(shape = var_1307, x = transpose_93)[name = tensor<string, []>("key_states_55_cast")];
            tensor<int32, [3]> var_1309 = const()[name = tensor<string, []>("op_1309"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_92 = transpose(perm = var_1294_perm_0, x = var_1293_cast)[name = tensor<string, []>("transpose_92")];
            tensor<fp16, [20, 77, 64]> value_states_55_cast = reshape(shape = var_1309, x = transpose_92)[name = tensor<string, []>("value_states_55_cast")];
            tensor<int32, [3]> var_1312_perm_0 = const()[name = tensor<string, []>("op_1312_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_79_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_79_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_91 = transpose(perm = var_1312_perm_0, x = key_states_55_cast)[name = tensor<string, []>("transpose_91")];
            tensor<fp16, [20, 77, 77]> attn_weights_79_cast = matmul(transpose_x = attn_weights_79_transpose_x_0, transpose_y = attn_weights_79_transpose_y_0, x = query_states_27_cast, y = transpose_91)[name = tensor<string, []>("attn_weights_79_cast")];
            tensor<int32, [4]> var_1314 = const()[name = tensor<string, []>("op_1314"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1315_cast = reshape(shape = var_1314, x = attn_weights_79_cast)[name = tensor<string, []>("op_1315_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_81_cast = add(x = var_1315_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_81_cast")];
            tensor<int32, [3]> var_1320 = const()[name = tensor<string, []>("op_1320"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_213_cast = reshape(shape = var_1320, x = attn_weights_81_cast)[name = tensor<string, []>("input_213_cast")];
            tensor<fp16, [20, 77, 77]> input_215_cast = softmax(axis = var_5, x = input_213_cast)[name = tensor<string, []>("input_215_cast")];
            tensor<bool, []> attn_output_79_transpose_x_0 = const()[name = tensor<string, []>("attn_output_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_79_transpose_y_0 = const()[name = tensor<string, []>("attn_output_79_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_79_cast = matmul(transpose_x = attn_output_79_transpose_x_0, transpose_y = attn_output_79_transpose_y_0, x = input_215_cast, y = value_states_55_cast)[name = tensor<string, []>("attn_output_79_cast")];
            tensor<int32, [4]> var_1325 = const()[name = tensor<string, []>("op_1325"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_81_cast = reshape(shape = var_1325, x = attn_output_79_cast)[name = tensor<string, []>("attn_output_81_cast")];
            tensor<int32, [4]> attn_output_83_perm_0 = const()[name = tensor<string, []>("attn_output_83_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1328 = const()[name = tensor<string, []>("op_1328"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_90 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast)[name = tensor<string, []>("transpose_90")];
            tensor<fp16, [1, 77, 1280]> input_217_cast = reshape(shape = var_1328, x = transpose_90)[name = tensor<string, []>("input_217_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387542848))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389181312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389181888)))];
            tensor<fp16, [1, 77, 1280]> linear_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized, x = input_217_cast)[name = tensor<string, []>("linear_81_cast")];
            tensor<fp16, [1, 77, 1280]> input_219_cast = add(x = input_211_cast, y = linear_81_cast)[name = tensor<string, []>("input_219_cast")];
            tensor<int32, [1]> input_221_axes_0 = const()[name = tensor<string, []>("input_221_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389184512)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389187136)))];
            tensor<fp16, [1, 77, 1280]> input_221_cast = layer_norm(axes = input_221_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_219_cast)[name = tensor<string, []>("input_221_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389189760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395743424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395744000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395749184))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_82_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized, x = input_221_cast)[name = tensor<string, []>("linear_82_cast")];
            tensor<string, []> input_225_mode_0 = const()[name = tensor<string, []>("input_225_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_225_cast = gelu(mode = input_225_mode_0, x = linear_82_cast)[name = tensor<string, []>("input_225_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395749760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402303424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402304000)))];
            tensor<fp16, [1, 77, 1280]> linear_83_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized, x = input_225_cast)[name = tensor<string, []>("linear_83_cast")];
            tensor<fp16, [1, 77, 1280]> input_227_cast = add(x = input_219_cast, y = linear_83_cast)[name = tensor<string, []>("input_227_cast")];
            tensor<int32, [1]> hidden_states_85_axes_0 = const()[name = tensor<string, []>("hidden_states_85_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402306624)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402309248)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_85_cast = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_227_cast)[name = tensor<string, []>("hidden_states_85_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402311872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403950336))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403950912)))];
            tensor<fp16, [1, 77, 1280]> linear_84_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_84_cast")];
            tensor<fp16, []> var_1367_to_fp16 = const()[name = tensor<string, []>("op_1367_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_89_cast = mul(x = linear_84_cast, y = var_1367_to_fp16)[name = tensor<string, []>("tensor_89_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403953536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405592000))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405592576)))];
            tensor<fp16, [1, 77, 1280]> linear_85_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_85_cast")];
            tensor<int32, [4]> var_1372 = const()[name = tensor<string, []>("op_1372"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1373_cast = reshape(shape = var_1372, x = linear_85_cast)[name = tensor<string, []>("op_1373_cast")];
            tensor<int32, [4]> var_1374_perm_0 = const()[name = tensor<string, []>("op_1374_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405595200))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407233664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407234240)))];
            tensor<fp16, [1, 77, 1280]> linear_86_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_86_cast")];
            tensor<int32, [4]> var_1379 = const()[name = tensor<string, []>("op_1379"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1380_cast = reshape(shape = var_1379, x = linear_86_cast)[name = tensor<string, []>("op_1380_cast")];
            tensor<int32, [4]> var_1381_perm_0 = const()[name = tensor<string, []>("op_1381_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1389_cast = reshape(shape = var_1388, x = tensor_89_cast)[name = tensor<string, []>("op_1389_cast")];
            tensor<int32, [4]> var_1390_perm_0 = const()[name = tensor<string, []>("op_1390_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1392 = const()[name = tensor<string, []>("op_1392"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_89 = transpose(perm = var_1390_perm_0, x = var_1389_cast)[name = tensor<string, []>("transpose_89")];
            tensor<fp16, [20, 77, 64]> query_states_29_cast = reshape(shape = var_1392, x = transpose_89)[name = tensor<string, []>("query_states_29_cast")];
            tensor<int32, [3]> var_1394 = const()[name = tensor<string, []>("op_1394"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_88 = transpose(perm = var_1374_perm_0, x = var_1373_cast)[name = tensor<string, []>("transpose_88")];
            tensor<fp16, [20, 77, 64]> key_states_59_cast = reshape(shape = var_1394, x = transpose_88)[name = tensor<string, []>("key_states_59_cast")];
            tensor<int32, [3]> var_1396 = const()[name = tensor<string, []>("op_1396"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_87 = transpose(perm = var_1381_perm_0, x = var_1380_cast)[name = tensor<string, []>("transpose_87")];
            tensor<fp16, [20, 77, 64]> value_states_59_cast = reshape(shape = var_1396, x = transpose_87)[name = tensor<string, []>("value_states_59_cast")];
            tensor<int32, [3]> var_1399_perm_0 = const()[name = tensor<string, []>("op_1399_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_85_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_85_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_86 = transpose(perm = var_1399_perm_0, x = key_states_59_cast)[name = tensor<string, []>("transpose_86")];
            tensor<fp16, [20, 77, 77]> attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = query_states_29_cast, y = transpose_86)[name = tensor<string, []>("attn_weights_85_cast")];
            tensor<int32, [4]> var_1401 = const()[name = tensor<string, []>("op_1401"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1402_cast = reshape(shape = var_1401, x = attn_weights_85_cast)[name = tensor<string, []>("op_1402_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_87_cast = add(x = var_1402_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_87_cast")];
            tensor<int32, [3]> var_1407 = const()[name = tensor<string, []>("op_1407"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_229_cast = reshape(shape = var_1407, x = attn_weights_87_cast)[name = tensor<string, []>("input_229_cast")];
            tensor<fp16, [20, 77, 77]> input_231_cast = softmax(axis = var_5, x = input_229_cast)[name = tensor<string, []>("input_231_cast")];
            tensor<bool, []> attn_output_85_transpose_x_0 = const()[name = tensor<string, []>("attn_output_85_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_85_transpose_y_0 = const()[name = tensor<string, []>("attn_output_85_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_85_cast = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = input_231_cast, y = value_states_59_cast)[name = tensor<string, []>("attn_output_85_cast")];
            tensor<int32, [4]> var_1412 = const()[name = tensor<string, []>("op_1412"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_87_cast = reshape(shape = var_1412, x = attn_output_85_cast)[name = tensor<string, []>("attn_output_87_cast")];
            tensor<int32, [4]> attn_output_89_perm_0 = const()[name = tensor<string, []>("attn_output_89_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1415 = const()[name = tensor<string, []>("op_1415"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_85 = transpose(perm = attn_output_89_perm_0, x = attn_output_87_cast)[name = tensor<string, []>("transpose_85")];
            tensor<fp16, [1, 77, 1280]> input_233_cast = reshape(shape = var_1415, x = transpose_85)[name = tensor<string, []>("input_233_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407236864))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408875328))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408875904)))];
            tensor<fp16, [1, 77, 1280]> linear_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized, x = input_233_cast)[name = tensor<string, []>("linear_87_cast")];
            tensor<fp16, [1, 77, 1280]> input_235_cast = add(x = input_227_cast, y = linear_87_cast)[name = tensor<string, []>("input_235_cast")];
            tensor<int32, [1]> input_237_axes_0 = const()[name = tensor<string, []>("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408878528)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408881152)))];
            tensor<fp16, [1, 77, 1280]> input_237_cast = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_235_cast)[name = tensor<string, []>("input_237_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408883776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415437440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415438016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415443200))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_88_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized, x = input_237_cast)[name = tensor<string, []>("linear_88_cast")];
            tensor<string, []> input_241_mode_0 = const()[name = tensor<string, []>("input_241_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_241_cast = gelu(mode = input_241_mode_0, x = linear_88_cast)[name = tensor<string, []>("input_241_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415443776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421997440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421998016)))];
            tensor<fp16, [1, 77, 1280]> linear_89_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized, x = input_241_cast)[name = tensor<string, []>("linear_89_cast")];
            tensor<fp16, [1, 77, 1280]> input_243_cast = add(x = input_235_cast, y = linear_89_cast)[name = tensor<string, []>("input_243_cast")];
            tensor<int32, [1]> hidden_states_91_axes_0 = const()[name = tensor<string, []>("hidden_states_91_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422000640)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422003264)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_91_cast = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_243_cast)[name = tensor<string, []>("hidden_states_91_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422005888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423644352))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423644928)))];
            tensor<fp16, [1, 77, 1280]> linear_90_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_90_cast")];
            tensor<fp16, []> var_1454_to_fp16 = const()[name = tensor<string, []>("op_1454_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_95_cast = mul(x = linear_90_cast, y = var_1454_to_fp16)[name = tensor<string, []>("tensor_95_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423647552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425286016))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425286592)))];
            tensor<fp16, [1, 77, 1280]> linear_91_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_91_cast")];
            tensor<int32, [4]> var_1459 = const()[name = tensor<string, []>("op_1459"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1460_cast = reshape(shape = var_1459, x = linear_91_cast)[name = tensor<string, []>("op_1460_cast")];
            tensor<int32, [4]> var_1461_perm_0 = const()[name = tensor<string, []>("op_1461_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425289216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426927680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426928256)))];
            tensor<fp16, [1, 77, 1280]> linear_92_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_92_cast")];
            tensor<int32, [4]> var_1466 = const()[name = tensor<string, []>("op_1466"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1467_cast = reshape(shape = var_1466, x = linear_92_cast)[name = tensor<string, []>("op_1467_cast")];
            tensor<int32, [4]> var_1468_perm_0 = const()[name = tensor<string, []>("op_1468_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1475 = const()[name = tensor<string, []>("op_1475"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1476_cast = reshape(shape = var_1475, x = tensor_95_cast)[name = tensor<string, []>("op_1476_cast")];
            tensor<int32, [4]> var_1477_perm_0 = const()[name = tensor<string, []>("op_1477_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1479 = const()[name = tensor<string, []>("op_1479"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_84 = transpose(perm = var_1477_perm_0, x = var_1476_cast)[name = tensor<string, []>("transpose_84")];
            tensor<fp16, [20, 77, 64]> query_states_31_cast = reshape(shape = var_1479, x = transpose_84)[name = tensor<string, []>("query_states_31_cast")];
            tensor<int32, [3]> var_1481 = const()[name = tensor<string, []>("op_1481"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_83 = transpose(perm = var_1461_perm_0, x = var_1460_cast)[name = tensor<string, []>("transpose_83")];
            tensor<fp16, [20, 77, 64]> key_states_63_cast = reshape(shape = var_1481, x = transpose_83)[name = tensor<string, []>("key_states_63_cast")];
            tensor<int32, [3]> var_1483 = const()[name = tensor<string, []>("op_1483"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_82 = transpose(perm = var_1468_perm_0, x = var_1467_cast)[name = tensor<string, []>("transpose_82")];
            tensor<fp16, [20, 77, 64]> value_states_63_cast = reshape(shape = var_1483, x = transpose_82)[name = tensor<string, []>("value_states_63_cast")];
            tensor<int32, [3]> var_1486_perm_0 = const()[name = tensor<string, []>("op_1486_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_91_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_91_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_81 = transpose(perm = var_1486_perm_0, x = key_states_63_cast)[name = tensor<string, []>("transpose_81")];
            tensor<fp16, [20, 77, 77]> attn_weights_91_cast = matmul(transpose_x = attn_weights_91_transpose_x_0, transpose_y = attn_weights_91_transpose_y_0, x = query_states_31_cast, y = transpose_81)[name = tensor<string, []>("attn_weights_91_cast")];
            tensor<int32, [4]> var_1488 = const()[name = tensor<string, []>("op_1488"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1489_cast = reshape(shape = var_1488, x = attn_weights_91_cast)[name = tensor<string, []>("op_1489_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_93_cast = add(x = var_1489_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_93_cast")];
            tensor<int32, [3]> var_1494 = const()[name = tensor<string, []>("op_1494"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_245_cast = reshape(shape = var_1494, x = attn_weights_93_cast)[name = tensor<string, []>("input_245_cast")];
            tensor<fp16, [20, 77, 77]> input_247_cast = softmax(axis = var_5, x = input_245_cast)[name = tensor<string, []>("input_247_cast")];
            tensor<bool, []> attn_output_91_transpose_x_0 = const()[name = tensor<string, []>("attn_output_91_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_91_transpose_y_0 = const()[name = tensor<string, []>("attn_output_91_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_91_cast = matmul(transpose_x = attn_output_91_transpose_x_0, transpose_y = attn_output_91_transpose_y_0, x = input_247_cast, y = value_states_63_cast)[name = tensor<string, []>("attn_output_91_cast")];
            tensor<int32, [4]> var_1499 = const()[name = tensor<string, []>("op_1499"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_93_cast = reshape(shape = var_1499, x = attn_output_91_cast)[name = tensor<string, []>("attn_output_93_cast")];
            tensor<int32, [4]> attn_output_95_perm_0 = const()[name = tensor<string, []>("attn_output_95_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1502 = const()[name = tensor<string, []>("op_1502"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_80 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast)[name = tensor<string, []>("transpose_80")];
            tensor<fp16, [1, 77, 1280]> input_249_cast = reshape(shape = var_1502, x = transpose_80)[name = tensor<string, []>("input_249_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426930880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428569344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428569920)))];
            tensor<fp16, [1, 77, 1280]> linear_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized, x = input_249_cast)[name = tensor<string, []>("linear_93_cast")];
            tensor<fp16, [1, 77, 1280]> input_251_cast = add(x = input_243_cast, y = linear_93_cast)[name = tensor<string, []>("input_251_cast")];
            tensor<int32, [1]> input_253_axes_0 = const()[name = tensor<string, []>("input_253_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428572544)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428575168)))];
            tensor<fp16, [1, 77, 1280]> input_253_cast = layer_norm(axes = input_253_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_251_cast)[name = tensor<string, []>("input_253_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428577792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435131456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435132032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435137216))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_94_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized, x = input_253_cast)[name = tensor<string, []>("linear_94_cast")];
            tensor<string, []> input_257_mode_0 = const()[name = tensor<string, []>("input_257_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_257_cast = gelu(mode = input_257_mode_0, x = linear_94_cast)[name = tensor<string, []>("input_257_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435137792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441691456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441692032)))];
            tensor<fp16, [1, 77, 1280]> linear_95_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized, x = input_257_cast)[name = tensor<string, []>("linear_95_cast")];
            tensor<fp16, [1, 77, 1280]> input_259_cast = add(x = input_251_cast, y = linear_95_cast)[name = tensor<string, []>("input_259_cast")];
            tensor<int32, [1]> hidden_states_97_axes_0 = const()[name = tensor<string, []>("hidden_states_97_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441694656)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441697280)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_97_cast = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_259_cast)[name = tensor<string, []>("hidden_states_97_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441699904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443338368))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443338944)))];
            tensor<fp16, [1, 77, 1280]> linear_96_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_96_cast")];
            tensor<fp16, []> var_1541_to_fp16 = const()[name = tensor<string, []>("op_1541_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_101_cast = mul(x = linear_96_cast, y = var_1541_to_fp16)[name = tensor<string, []>("tensor_101_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443341568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444980032))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444980608)))];
            tensor<fp16, [1, 77, 1280]> linear_97_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_97_cast")];
            tensor<int32, [4]> var_1546 = const()[name = tensor<string, []>("op_1546"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1547_cast = reshape(shape = var_1546, x = linear_97_cast)[name = tensor<string, []>("op_1547_cast")];
            tensor<int32, [4]> var_1548_perm_0 = const()[name = tensor<string, []>("op_1548_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444983232))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446621696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446622272)))];
            tensor<fp16, [1, 77, 1280]> linear_98_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_98_cast")];
            tensor<int32, [4]> var_1553 = const()[name = tensor<string, []>("op_1553"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1554_cast = reshape(shape = var_1553, x = linear_98_cast)[name = tensor<string, []>("op_1554_cast")];
            tensor<int32, [4]> var_1555_perm_0 = const()[name = tensor<string, []>("op_1555_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1562 = const()[name = tensor<string, []>("op_1562"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1563_cast = reshape(shape = var_1562, x = tensor_101_cast)[name = tensor<string, []>("op_1563_cast")];
            tensor<int32, [4]> var_1564_perm_0 = const()[name = tensor<string, []>("op_1564_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1566 = const()[name = tensor<string, []>("op_1566"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_79 = transpose(perm = var_1564_perm_0, x = var_1563_cast)[name = tensor<string, []>("transpose_79")];
            tensor<fp16, [20, 77, 64]> query_states_33_cast = reshape(shape = var_1566, x = transpose_79)[name = tensor<string, []>("query_states_33_cast")];
            tensor<int32, [3]> var_1568 = const()[name = tensor<string, []>("op_1568"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_78 = transpose(perm = var_1548_perm_0, x = var_1547_cast)[name = tensor<string, []>("transpose_78")];
            tensor<fp16, [20, 77, 64]> key_states_67_cast = reshape(shape = var_1568, x = transpose_78)[name = tensor<string, []>("key_states_67_cast")];
            tensor<int32, [3]> var_1570 = const()[name = tensor<string, []>("op_1570"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_77 = transpose(perm = var_1555_perm_0, x = var_1554_cast)[name = tensor<string, []>("transpose_77")];
            tensor<fp16, [20, 77, 64]> value_states_67_cast = reshape(shape = var_1570, x = transpose_77)[name = tensor<string, []>("value_states_67_cast")];
            tensor<int32, [3]> var_1573_perm_0 = const()[name = tensor<string, []>("op_1573_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_97_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_97_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_76 = transpose(perm = var_1573_perm_0, x = key_states_67_cast)[name = tensor<string, []>("transpose_76")];
            tensor<fp16, [20, 77, 77]> attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = query_states_33_cast, y = transpose_76)[name = tensor<string, []>("attn_weights_97_cast")];
            tensor<int32, [4]> var_1575 = const()[name = tensor<string, []>("op_1575"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1576_cast = reshape(shape = var_1575, x = attn_weights_97_cast)[name = tensor<string, []>("op_1576_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_99_cast = add(x = var_1576_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_99_cast")];
            tensor<int32, [3]> var_1581 = const()[name = tensor<string, []>("op_1581"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_261_cast = reshape(shape = var_1581, x = attn_weights_99_cast)[name = tensor<string, []>("input_261_cast")];
            tensor<fp16, [20, 77, 77]> input_263_cast = softmax(axis = var_5, x = input_261_cast)[name = tensor<string, []>("input_263_cast")];
            tensor<bool, []> attn_output_97_transpose_x_0 = const()[name = tensor<string, []>("attn_output_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_97_transpose_y_0 = const()[name = tensor<string, []>("attn_output_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_97_cast = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = input_263_cast, y = value_states_67_cast)[name = tensor<string, []>("attn_output_97_cast")];
            tensor<int32, [4]> var_1586 = const()[name = tensor<string, []>("op_1586"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_99_cast = reshape(shape = var_1586, x = attn_output_97_cast)[name = tensor<string, []>("attn_output_99_cast")];
            tensor<int32, [4]> attn_output_101_perm_0 = const()[name = tensor<string, []>("attn_output_101_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1589 = const()[name = tensor<string, []>("op_1589"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_75 = transpose(perm = attn_output_101_perm_0, x = attn_output_99_cast)[name = tensor<string, []>("transpose_75")];
            tensor<fp16, [1, 77, 1280]> input_265_cast = reshape(shape = var_1589, x = transpose_75)[name = tensor<string, []>("input_265_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446624896))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448263360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448263936)))];
            tensor<fp16, [1, 77, 1280]> linear_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized, x = input_265_cast)[name = tensor<string, []>("linear_99_cast")];
            tensor<fp16, [1, 77, 1280]> input_267_cast = add(x = input_259_cast, y = linear_99_cast)[name = tensor<string, []>("input_267_cast")];
            tensor<int32, [1]> input_269_axes_0 = const()[name = tensor<string, []>("input_269_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448266560)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448269184)))];
            tensor<fp16, [1, 77, 1280]> input_269_cast = layer_norm(axes = input_269_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_267_cast)[name = tensor<string, []>("input_269_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448271808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454825472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454826048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454831232))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_100_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized, x = input_269_cast)[name = tensor<string, []>("linear_100_cast")];
            tensor<string, []> input_273_mode_0 = const()[name = tensor<string, []>("input_273_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_273_cast = gelu(mode = input_273_mode_0, x = linear_100_cast)[name = tensor<string, []>("input_273_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454831808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461385472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461386048)))];
            tensor<fp16, [1, 77, 1280]> linear_101_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized, x = input_273_cast)[name = tensor<string, []>("linear_101_cast")];
            tensor<fp16, [1, 77, 1280]> input_275_cast = add(x = input_267_cast, y = linear_101_cast)[name = tensor<string, []>("input_275_cast")];
            tensor<int32, [1]> hidden_states_103_axes_0 = const()[name = tensor<string, []>("hidden_states_103_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461388672)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461391296)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_103_cast = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_275_cast)[name = tensor<string, []>("hidden_states_103_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461393920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463032384))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463032960)))];
            tensor<fp16, [1, 77, 1280]> linear_102_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_102_cast")];
            tensor<fp16, []> var_1628_to_fp16 = const()[name = tensor<string, []>("op_1628_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_107_cast = mul(x = linear_102_cast, y = var_1628_to_fp16)[name = tensor<string, []>("tensor_107_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463035584))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464674048))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464674624)))];
            tensor<fp16, [1, 77, 1280]> linear_103_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_103_cast")];
            tensor<int32, [4]> var_1633 = const()[name = tensor<string, []>("op_1633"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1634_cast = reshape(shape = var_1633, x = linear_103_cast)[name = tensor<string, []>("op_1634_cast")];
            tensor<int32, [4]> var_1635_perm_0 = const()[name = tensor<string, []>("op_1635_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464677248))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466315712))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466316288)))];
            tensor<fp16, [1, 77, 1280]> linear_104_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_104_cast")];
            tensor<int32, [4]> var_1640 = const()[name = tensor<string, []>("op_1640"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1641_cast = reshape(shape = var_1640, x = linear_104_cast)[name = tensor<string, []>("op_1641_cast")];
            tensor<int32, [4]> var_1642_perm_0 = const()[name = tensor<string, []>("op_1642_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1650_cast = reshape(shape = var_1649, x = tensor_107_cast)[name = tensor<string, []>("op_1650_cast")];
            tensor<int32, [4]> var_1651_perm_0 = const()[name = tensor<string, []>("op_1651_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1653 = const()[name = tensor<string, []>("op_1653"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_74 = transpose(perm = var_1651_perm_0, x = var_1650_cast)[name = tensor<string, []>("transpose_74")];
            tensor<fp16, [20, 77, 64]> query_states_35_cast = reshape(shape = var_1653, x = transpose_74)[name = tensor<string, []>("query_states_35_cast")];
            tensor<int32, [3]> var_1655 = const()[name = tensor<string, []>("op_1655"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_73 = transpose(perm = var_1635_perm_0, x = var_1634_cast)[name = tensor<string, []>("transpose_73")];
            tensor<fp16, [20, 77, 64]> key_states_71_cast = reshape(shape = var_1655, x = transpose_73)[name = tensor<string, []>("key_states_71_cast")];
            tensor<int32, [3]> var_1657 = const()[name = tensor<string, []>("op_1657"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_72 = transpose(perm = var_1642_perm_0, x = var_1641_cast)[name = tensor<string, []>("transpose_72")];
            tensor<fp16, [20, 77, 64]> value_states_71_cast = reshape(shape = var_1657, x = transpose_72)[name = tensor<string, []>("value_states_71_cast")];
            tensor<int32, [3]> var_1660_perm_0 = const()[name = tensor<string, []>("op_1660_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_103_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_103_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_71 = transpose(perm = var_1660_perm_0, x = key_states_71_cast)[name = tensor<string, []>("transpose_71")];
            tensor<fp16, [20, 77, 77]> attn_weights_103_cast = matmul(transpose_x = attn_weights_103_transpose_x_0, transpose_y = attn_weights_103_transpose_y_0, x = query_states_35_cast, y = transpose_71)[name = tensor<string, []>("attn_weights_103_cast")];
            tensor<int32, [4]> var_1662 = const()[name = tensor<string, []>("op_1662"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1663_cast = reshape(shape = var_1662, x = attn_weights_103_cast)[name = tensor<string, []>("op_1663_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_105_cast = add(x = var_1663_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_105_cast")];
            tensor<int32, [3]> var_1668 = const()[name = tensor<string, []>("op_1668"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_277_cast = reshape(shape = var_1668, x = attn_weights_105_cast)[name = tensor<string, []>("input_277_cast")];
            tensor<fp16, [20, 77, 77]> input_279_cast = softmax(axis = var_5, x = input_277_cast)[name = tensor<string, []>("input_279_cast")];
            tensor<bool, []> attn_output_103_transpose_x_0 = const()[name = tensor<string, []>("attn_output_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_103_transpose_y_0 = const()[name = tensor<string, []>("attn_output_103_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_103_cast = matmul(transpose_x = attn_output_103_transpose_x_0, transpose_y = attn_output_103_transpose_y_0, x = input_279_cast, y = value_states_71_cast)[name = tensor<string, []>("attn_output_103_cast")];
            tensor<int32, [4]> var_1673 = const()[name = tensor<string, []>("op_1673"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_105_cast = reshape(shape = var_1673, x = attn_output_103_cast)[name = tensor<string, []>("attn_output_105_cast")];
            tensor<int32, [4]> attn_output_107_perm_0 = const()[name = tensor<string, []>("attn_output_107_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1676 = const()[name = tensor<string, []>("op_1676"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_70 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast)[name = tensor<string, []>("transpose_70")];
            tensor<fp16, [1, 77, 1280]> input_281_cast = reshape(shape = var_1676, x = transpose_70)[name = tensor<string, []>("input_281_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466318912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467957376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467957952)))];
            tensor<fp16, [1, 77, 1280]> linear_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized, x = input_281_cast)[name = tensor<string, []>("linear_105_cast")];
            tensor<fp16, [1, 77, 1280]> input_283_cast = add(x = input_275_cast, y = linear_105_cast)[name = tensor<string, []>("input_283_cast")];
            tensor<int32, [1]> input_285_axes_0 = const()[name = tensor<string, []>("input_285_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467960576)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467963200)))];
            tensor<fp16, [1, 77, 1280]> input_285_cast = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_283_cast)[name = tensor<string, []>("input_285_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467965824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474519488))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474520064))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474525248))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_106_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized, x = input_285_cast)[name = tensor<string, []>("linear_106_cast")];
            tensor<string, []> input_289_mode_0 = const()[name = tensor<string, []>("input_289_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_289_cast = gelu(mode = input_289_mode_0, x = linear_106_cast)[name = tensor<string, []>("input_289_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474525824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481079488))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481080064)))];
            tensor<fp16, [1, 77, 1280]> linear_107_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized, x = input_289_cast)[name = tensor<string, []>("linear_107_cast")];
            tensor<fp16, [1, 77, 1280]> input_291_cast = add(x = input_283_cast, y = linear_107_cast)[name = tensor<string, []>("input_291_cast")];
            tensor<int32, [1]> hidden_states_109_axes_0 = const()[name = tensor<string, []>("hidden_states_109_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481082688)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481085312)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_109_cast = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_291_cast)[name = tensor<string, []>("hidden_states_109_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481087936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482726400))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482726976)))];
            tensor<fp16, [1, 77, 1280]> linear_108_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_108_cast")];
            tensor<fp16, []> var_1715_to_fp16 = const()[name = tensor<string, []>("op_1715_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_113_cast = mul(x = linear_108_cast, y = var_1715_to_fp16)[name = tensor<string, []>("tensor_113_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482729600))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484368064))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484368640)))];
            tensor<fp16, [1, 77, 1280]> linear_109_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_109_cast")];
            tensor<int32, [4]> var_1720 = const()[name = tensor<string, []>("op_1720"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1721_cast = reshape(shape = var_1720, x = linear_109_cast)[name = tensor<string, []>("op_1721_cast")];
            tensor<int32, [4]> var_1722_perm_0 = const()[name = tensor<string, []>("op_1722_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484371264))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486009728))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486010304)))];
            tensor<fp16, [1, 77, 1280]> linear_110_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_110_cast")];
            tensor<int32, [4]> var_1727 = const()[name = tensor<string, []>("op_1727"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1728_cast = reshape(shape = var_1727, x = linear_110_cast)[name = tensor<string, []>("op_1728_cast")];
            tensor<int32, [4]> var_1729_perm_0 = const()[name = tensor<string, []>("op_1729_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1736 = const()[name = tensor<string, []>("op_1736"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1737_cast = reshape(shape = var_1736, x = tensor_113_cast)[name = tensor<string, []>("op_1737_cast")];
            tensor<int32, [4]> var_1738_perm_0 = const()[name = tensor<string, []>("op_1738_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1740 = const()[name = tensor<string, []>("op_1740"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_69 = transpose(perm = var_1738_perm_0, x = var_1737_cast)[name = tensor<string, []>("transpose_69")];
            tensor<fp16, [20, 77, 64]> query_states_37_cast = reshape(shape = var_1740, x = transpose_69)[name = tensor<string, []>("query_states_37_cast")];
            tensor<int32, [3]> var_1742 = const()[name = tensor<string, []>("op_1742"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_68 = transpose(perm = var_1722_perm_0, x = var_1721_cast)[name = tensor<string, []>("transpose_68")];
            tensor<fp16, [20, 77, 64]> key_states_75_cast = reshape(shape = var_1742, x = transpose_68)[name = tensor<string, []>("key_states_75_cast")];
            tensor<int32, [3]> var_1744 = const()[name = tensor<string, []>("op_1744"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_67 = transpose(perm = var_1729_perm_0, x = var_1728_cast)[name = tensor<string, []>("transpose_67")];
            tensor<fp16, [20, 77, 64]> value_states_75_cast = reshape(shape = var_1744, x = transpose_67)[name = tensor<string, []>("value_states_75_cast")];
            tensor<int32, [3]> var_1747_perm_0 = const()[name = tensor<string, []>("op_1747_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_109_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_109_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_66 = transpose(perm = var_1747_perm_0, x = key_states_75_cast)[name = tensor<string, []>("transpose_66")];
            tensor<fp16, [20, 77, 77]> attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = query_states_37_cast, y = transpose_66)[name = tensor<string, []>("attn_weights_109_cast")];
            tensor<int32, [4]> var_1749 = const()[name = tensor<string, []>("op_1749"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1750_cast = reshape(shape = var_1749, x = attn_weights_109_cast)[name = tensor<string, []>("op_1750_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_111_cast = add(x = var_1750_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_111_cast")];
            tensor<int32, [3]> var_1755 = const()[name = tensor<string, []>("op_1755"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_293_cast = reshape(shape = var_1755, x = attn_weights_111_cast)[name = tensor<string, []>("input_293_cast")];
            tensor<fp16, [20, 77, 77]> input_295_cast = softmax(axis = var_5, x = input_293_cast)[name = tensor<string, []>("input_295_cast")];
            tensor<bool, []> attn_output_109_transpose_x_0 = const()[name = tensor<string, []>("attn_output_109_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_109_transpose_y_0 = const()[name = tensor<string, []>("attn_output_109_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_109_cast = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = input_295_cast, y = value_states_75_cast)[name = tensor<string, []>("attn_output_109_cast")];
            tensor<int32, [4]> var_1760 = const()[name = tensor<string, []>("op_1760"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_111_cast = reshape(shape = var_1760, x = attn_output_109_cast)[name = tensor<string, []>("attn_output_111_cast")];
            tensor<int32, [4]> attn_output_113_perm_0 = const()[name = tensor<string, []>("attn_output_113_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1763 = const()[name = tensor<string, []>("op_1763"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_65 = transpose(perm = attn_output_113_perm_0, x = attn_output_111_cast)[name = tensor<string, []>("transpose_65")];
            tensor<fp16, [1, 77, 1280]> input_297_cast = reshape(shape = var_1763, x = transpose_65)[name = tensor<string, []>("input_297_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486012928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487651392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487651968)))];
            tensor<fp16, [1, 77, 1280]> linear_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized, x = input_297_cast)[name = tensor<string, []>("linear_111_cast")];
            tensor<fp16, [1, 77, 1280]> input_299_cast = add(x = input_291_cast, y = linear_111_cast)[name = tensor<string, []>("input_299_cast")];
            tensor<int32, [1]> input_301_axes_0 = const()[name = tensor<string, []>("input_301_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487654592)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487657216)))];
            tensor<fp16, [1, 77, 1280]> input_301_cast = layer_norm(axes = input_301_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_299_cast)[name = tensor<string, []>("input_301_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487659840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494213504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494214080))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494219264))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_112_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized, x = input_301_cast)[name = tensor<string, []>("linear_112_cast")];
            tensor<string, []> input_305_mode_0 = const()[name = tensor<string, []>("input_305_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_305_cast = gelu(mode = input_305_mode_0, x = linear_112_cast)[name = tensor<string, []>("input_305_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494219840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500773504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500774080)))];
            tensor<fp16, [1, 77, 1280]> linear_113_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized, x = input_305_cast)[name = tensor<string, []>("linear_113_cast")];
            tensor<fp16, [1, 77, 1280]> input_307_cast = add(x = input_299_cast, y = linear_113_cast)[name = tensor<string, []>("input_307_cast")];
            tensor<int32, [1]> hidden_states_115_axes_0 = const()[name = tensor<string, []>("hidden_states_115_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500776704)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500779328)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_115_cast = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_307_cast)[name = tensor<string, []>("hidden_states_115_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500781952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502420416))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502420992)))];
            tensor<fp16, [1, 77, 1280]> linear_114_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_114_cast")];
            tensor<fp16, []> var_1802_to_fp16 = const()[name = tensor<string, []>("op_1802_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_119_cast = mul(x = linear_114_cast, y = var_1802_to_fp16)[name = tensor<string, []>("tensor_119_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502423616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504062080))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504062656)))];
            tensor<fp16, [1, 77, 1280]> linear_115_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_115_cast")];
            tensor<int32, [4]> var_1807 = const()[name = tensor<string, []>("op_1807"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1808_cast = reshape(shape = var_1807, x = linear_115_cast)[name = tensor<string, []>("op_1808_cast")];
            tensor<int32, [4]> var_1809_perm_0 = const()[name = tensor<string, []>("op_1809_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504065280))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505703744))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505704320)))];
            tensor<fp16, [1, 77, 1280]> linear_116_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_116_cast")];
            tensor<int32, [4]> var_1814 = const()[name = tensor<string, []>("op_1814"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1815_cast = reshape(shape = var_1814, x = linear_116_cast)[name = tensor<string, []>("op_1815_cast")];
            tensor<int32, [4]> var_1816_perm_0 = const()[name = tensor<string, []>("op_1816_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1824_cast = reshape(shape = var_1823, x = tensor_119_cast)[name = tensor<string, []>("op_1824_cast")];
            tensor<int32, [4]> var_1825_perm_0 = const()[name = tensor<string, []>("op_1825_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1827 = const()[name = tensor<string, []>("op_1827"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_64 = transpose(perm = var_1825_perm_0, x = var_1824_cast)[name = tensor<string, []>("transpose_64")];
            tensor<fp16, [20, 77, 64]> query_states_39_cast = reshape(shape = var_1827, x = transpose_64)[name = tensor<string, []>("query_states_39_cast")];
            tensor<int32, [3]> var_1829 = const()[name = tensor<string, []>("op_1829"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_63 = transpose(perm = var_1809_perm_0, x = var_1808_cast)[name = tensor<string, []>("transpose_63")];
            tensor<fp16, [20, 77, 64]> key_states_79_cast = reshape(shape = var_1829, x = transpose_63)[name = tensor<string, []>("key_states_79_cast")];
            tensor<int32, [3]> var_1831 = const()[name = tensor<string, []>("op_1831"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_62 = transpose(perm = var_1816_perm_0, x = var_1815_cast)[name = tensor<string, []>("transpose_62")];
            tensor<fp16, [20, 77, 64]> value_states_79_cast = reshape(shape = var_1831, x = transpose_62)[name = tensor<string, []>("value_states_79_cast")];
            tensor<int32, [3]> var_1834_perm_0 = const()[name = tensor<string, []>("op_1834_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_115_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_115_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_61 = transpose(perm = var_1834_perm_0, x = key_states_79_cast)[name = tensor<string, []>("transpose_61")];
            tensor<fp16, [20, 77, 77]> attn_weights_115_cast = matmul(transpose_x = attn_weights_115_transpose_x_0, transpose_y = attn_weights_115_transpose_y_0, x = query_states_39_cast, y = transpose_61)[name = tensor<string, []>("attn_weights_115_cast")];
            tensor<int32, [4]> var_1836 = const()[name = tensor<string, []>("op_1836"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1837_cast = reshape(shape = var_1836, x = attn_weights_115_cast)[name = tensor<string, []>("op_1837_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_117_cast = add(x = var_1837_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_117_cast")];
            tensor<int32, [3]> var_1842 = const()[name = tensor<string, []>("op_1842"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_309_cast = reshape(shape = var_1842, x = attn_weights_117_cast)[name = tensor<string, []>("input_309_cast")];
            tensor<fp16, [20, 77, 77]> input_311_cast = softmax(axis = var_5, x = input_309_cast)[name = tensor<string, []>("input_311_cast")];
            tensor<bool, []> attn_output_115_transpose_x_0 = const()[name = tensor<string, []>("attn_output_115_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_115_transpose_y_0 = const()[name = tensor<string, []>("attn_output_115_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_115_cast = matmul(transpose_x = attn_output_115_transpose_x_0, transpose_y = attn_output_115_transpose_y_0, x = input_311_cast, y = value_states_79_cast)[name = tensor<string, []>("attn_output_115_cast")];
            tensor<int32, [4]> var_1847 = const()[name = tensor<string, []>("op_1847"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_117_cast = reshape(shape = var_1847, x = attn_output_115_cast)[name = tensor<string, []>("attn_output_117_cast")];
            tensor<int32, [4]> attn_output_119_perm_0 = const()[name = tensor<string, []>("attn_output_119_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1850 = const()[name = tensor<string, []>("op_1850"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_60 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast)[name = tensor<string, []>("transpose_60")];
            tensor<fp16, [1, 77, 1280]> input_313_cast = reshape(shape = var_1850, x = transpose_60)[name = tensor<string, []>("input_313_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505706944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507345408))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507345984)))];
            tensor<fp16, [1, 77, 1280]> linear_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized, x = input_313_cast)[name = tensor<string, []>("linear_117_cast")];
            tensor<fp16, [1, 77, 1280]> input_315_cast = add(x = input_307_cast, y = linear_117_cast)[name = tensor<string, []>("input_315_cast")];
            tensor<int32, [1]> input_317_axes_0 = const()[name = tensor<string, []>("input_317_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507348608)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507351232)))];
            tensor<fp16, [1, 77, 1280]> input_317_cast = layer_norm(axes = input_317_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_315_cast)[name = tensor<string, []>("input_317_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507353856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513907520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513908096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513913280))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_118_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized, x = input_317_cast)[name = tensor<string, []>("linear_118_cast")];
            tensor<string, []> input_321_mode_0 = const()[name = tensor<string, []>("input_321_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_321_cast = gelu(mode = input_321_mode_0, x = linear_118_cast)[name = tensor<string, []>("input_321_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513913856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520467520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520468096)))];
            tensor<fp16, [1, 77, 1280]> linear_119_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized, x = input_321_cast)[name = tensor<string, []>("linear_119_cast")];
            tensor<fp16, [1, 77, 1280]> input_323_cast = add(x = input_315_cast, y = linear_119_cast)[name = tensor<string, []>("input_323_cast")];
            tensor<int32, [1]> hidden_states_121_axes_0 = const()[name = tensor<string, []>("hidden_states_121_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520470720)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520473344)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_121_cast = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_323_cast)[name = tensor<string, []>("hidden_states_121_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520475968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522114432))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522115008)))];
            tensor<fp16, [1, 77, 1280]> linear_120_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_120_cast")];
            tensor<fp16, []> var_1889_to_fp16 = const()[name = tensor<string, []>("op_1889_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_125_cast = mul(x = linear_120_cast, y = var_1889_to_fp16)[name = tensor<string, []>("tensor_125_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522117632))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523756096))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523756672)))];
            tensor<fp16, [1, 77, 1280]> linear_121_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_121_cast")];
            tensor<int32, [4]> var_1894 = const()[name = tensor<string, []>("op_1894"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1895_cast = reshape(shape = var_1894, x = linear_121_cast)[name = tensor<string, []>("op_1895_cast")];
            tensor<int32, [4]> var_1896_perm_0 = const()[name = tensor<string, []>("op_1896_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523759296))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525397760))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525398336)))];
            tensor<fp16, [1, 77, 1280]> linear_122_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_122_cast")];
            tensor<int32, [4]> var_1901 = const()[name = tensor<string, []>("op_1901"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1902_cast = reshape(shape = var_1901, x = linear_122_cast)[name = tensor<string, []>("op_1902_cast")];
            tensor<int32, [4]> var_1903_perm_0 = const()[name = tensor<string, []>("op_1903_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1910 = const()[name = tensor<string, []>("op_1910"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1911_cast = reshape(shape = var_1910, x = tensor_125_cast)[name = tensor<string, []>("op_1911_cast")];
            tensor<int32, [4]> var_1912_perm_0 = const()[name = tensor<string, []>("op_1912_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1914 = const()[name = tensor<string, []>("op_1914"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_59 = transpose(perm = var_1912_perm_0, x = var_1911_cast)[name = tensor<string, []>("transpose_59")];
            tensor<fp16, [20, 77, 64]> query_states_41_cast = reshape(shape = var_1914, x = transpose_59)[name = tensor<string, []>("query_states_41_cast")];
            tensor<int32, [3]> var_1916 = const()[name = tensor<string, []>("op_1916"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_58 = transpose(perm = var_1896_perm_0, x = var_1895_cast)[name = tensor<string, []>("transpose_58")];
            tensor<fp16, [20, 77, 64]> key_states_83_cast = reshape(shape = var_1916, x = transpose_58)[name = tensor<string, []>("key_states_83_cast")];
            tensor<int32, [3]> var_1918 = const()[name = tensor<string, []>("op_1918"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_57 = transpose(perm = var_1903_perm_0, x = var_1902_cast)[name = tensor<string, []>("transpose_57")];
            tensor<fp16, [20, 77, 64]> value_states_83_cast = reshape(shape = var_1918, x = transpose_57)[name = tensor<string, []>("value_states_83_cast")];
            tensor<int32, [3]> var_1921_perm_0 = const()[name = tensor<string, []>("op_1921_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_121_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_121_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_56 = transpose(perm = var_1921_perm_0, x = key_states_83_cast)[name = tensor<string, []>("transpose_56")];
            tensor<fp16, [20, 77, 77]> attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = query_states_41_cast, y = transpose_56)[name = tensor<string, []>("attn_weights_121_cast")];
            tensor<int32, [4]> var_1923 = const()[name = tensor<string, []>("op_1923"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_1924_cast = reshape(shape = var_1923, x = attn_weights_121_cast)[name = tensor<string, []>("op_1924_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_123_cast = add(x = var_1924_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_123_cast")];
            tensor<int32, [3]> var_1929 = const()[name = tensor<string, []>("op_1929"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_325_cast = reshape(shape = var_1929, x = attn_weights_123_cast)[name = tensor<string, []>("input_325_cast")];
            tensor<fp16, [20, 77, 77]> input_327_cast = softmax(axis = var_5, x = input_325_cast)[name = tensor<string, []>("input_327_cast")];
            tensor<bool, []> attn_output_121_transpose_x_0 = const()[name = tensor<string, []>("attn_output_121_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_121_transpose_y_0 = const()[name = tensor<string, []>("attn_output_121_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_121_cast = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = input_327_cast, y = value_states_83_cast)[name = tensor<string, []>("attn_output_121_cast")];
            tensor<int32, [4]> var_1934 = const()[name = tensor<string, []>("op_1934"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_123_cast = reshape(shape = var_1934, x = attn_output_121_cast)[name = tensor<string, []>("attn_output_123_cast")];
            tensor<int32, [4]> attn_output_125_perm_0 = const()[name = tensor<string, []>("attn_output_125_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1937 = const()[name = tensor<string, []>("op_1937"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_55 = transpose(perm = attn_output_125_perm_0, x = attn_output_123_cast)[name = tensor<string, []>("transpose_55")];
            tensor<fp16, [1, 77, 1280]> input_329_cast = reshape(shape = var_1937, x = transpose_55)[name = tensor<string, []>("input_329_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525400960))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527039424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527040000)))];
            tensor<fp16, [1, 77, 1280]> linear_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized, x = input_329_cast)[name = tensor<string, []>("linear_123_cast")];
            tensor<fp16, [1, 77, 1280]> input_331_cast = add(x = input_323_cast, y = linear_123_cast)[name = tensor<string, []>("input_331_cast")];
            tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527042624)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527045248)))];
            tensor<fp16, [1, 77, 1280]> input_333_cast = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_331_cast)[name = tensor<string, []>("input_333_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527047872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533601536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533602112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533607296))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_124_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized, x = input_333_cast)[name = tensor<string, []>("linear_124_cast")];
            tensor<string, []> input_337_mode_0 = const()[name = tensor<string, []>("input_337_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_337_cast = gelu(mode = input_337_mode_0, x = linear_124_cast)[name = tensor<string, []>("input_337_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533607872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540161536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540162112)))];
            tensor<fp16, [1, 77, 1280]> linear_125_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized, x = input_337_cast)[name = tensor<string, []>("linear_125_cast")];
            tensor<fp16, [1, 77, 1280]> input_339_cast = add(x = input_331_cast, y = linear_125_cast)[name = tensor<string, []>("input_339_cast")];
            tensor<int32, [1]> hidden_states_127_axes_0 = const()[name = tensor<string, []>("hidden_states_127_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540164736)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540167360)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_127_cast = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_339_cast)[name = tensor<string, []>("hidden_states_127_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540169984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541808448))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541809024)))];
            tensor<fp16, [1, 77, 1280]> linear_126_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_126_cast")];
            tensor<fp16, []> var_1976_to_fp16 = const()[name = tensor<string, []>("op_1976_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_131_cast = mul(x = linear_126_cast, y = var_1976_to_fp16)[name = tensor<string, []>("tensor_131_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541811648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543450112))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543450688)))];
            tensor<fp16, [1, 77, 1280]> linear_127_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_127_cast")];
            tensor<int32, [4]> var_1981 = const()[name = tensor<string, []>("op_1981"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1982_cast = reshape(shape = var_1981, x = linear_127_cast)[name = tensor<string, []>("op_1982_cast")];
            tensor<int32, [4]> var_1983_perm_0 = const()[name = tensor<string, []>("op_1983_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543453312))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545091776))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545092352)))];
            tensor<fp16, [1, 77, 1280]> linear_128_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_128_cast")];
            tensor<int32, [4]> var_1988 = const()[name = tensor<string, []>("op_1988"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1989_cast = reshape(shape = var_1988, x = linear_128_cast)[name = tensor<string, []>("op_1989_cast")];
            tensor<int32, [4]> var_1990_perm_0 = const()[name = tensor<string, []>("op_1990_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_1998_cast = reshape(shape = var_1997, x = tensor_131_cast)[name = tensor<string, []>("op_1998_cast")];
            tensor<int32, [4]> var_1999_perm_0 = const()[name = tensor<string, []>("op_1999_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2001 = const()[name = tensor<string, []>("op_2001"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_54 = transpose(perm = var_1999_perm_0, x = var_1998_cast)[name = tensor<string, []>("transpose_54")];
            tensor<fp16, [20, 77, 64]> query_states_43_cast = reshape(shape = var_2001, x = transpose_54)[name = tensor<string, []>("query_states_43_cast")];
            tensor<int32, [3]> var_2003 = const()[name = tensor<string, []>("op_2003"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_53 = transpose(perm = var_1983_perm_0, x = var_1982_cast)[name = tensor<string, []>("transpose_53")];
            tensor<fp16, [20, 77, 64]> key_states_87_cast = reshape(shape = var_2003, x = transpose_53)[name = tensor<string, []>("key_states_87_cast")];
            tensor<int32, [3]> var_2005 = const()[name = tensor<string, []>("op_2005"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_52 = transpose(perm = var_1990_perm_0, x = var_1989_cast)[name = tensor<string, []>("transpose_52")];
            tensor<fp16, [20, 77, 64]> value_states_87_cast = reshape(shape = var_2005, x = transpose_52)[name = tensor<string, []>("value_states_87_cast")];
            tensor<int32, [3]> var_2008_perm_0 = const()[name = tensor<string, []>("op_2008_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_127_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_127_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_51 = transpose(perm = var_2008_perm_0, x = key_states_87_cast)[name = tensor<string, []>("transpose_51")];
            tensor<fp16, [20, 77, 77]> attn_weights_127_cast = matmul(transpose_x = attn_weights_127_transpose_x_0, transpose_y = attn_weights_127_transpose_y_0, x = query_states_43_cast, y = transpose_51)[name = tensor<string, []>("attn_weights_127_cast")];
            tensor<int32, [4]> var_2010 = const()[name = tensor<string, []>("op_2010"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2011_cast = reshape(shape = var_2010, x = attn_weights_127_cast)[name = tensor<string, []>("op_2011_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_129_cast = add(x = var_2011_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_129_cast")];
            tensor<int32, [3]> var_2016 = const()[name = tensor<string, []>("op_2016"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_341_cast = reshape(shape = var_2016, x = attn_weights_129_cast)[name = tensor<string, []>("input_341_cast")];
            tensor<fp16, [20, 77, 77]> input_343_cast = softmax(axis = var_5, x = input_341_cast)[name = tensor<string, []>("input_343_cast")];
            tensor<bool, []> attn_output_127_transpose_x_0 = const()[name = tensor<string, []>("attn_output_127_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_127_transpose_y_0 = const()[name = tensor<string, []>("attn_output_127_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_127_cast = matmul(transpose_x = attn_output_127_transpose_x_0, transpose_y = attn_output_127_transpose_y_0, x = input_343_cast, y = value_states_87_cast)[name = tensor<string, []>("attn_output_127_cast")];
            tensor<int32, [4]> var_2021 = const()[name = tensor<string, []>("op_2021"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_129_cast = reshape(shape = var_2021, x = attn_output_127_cast)[name = tensor<string, []>("attn_output_129_cast")];
            tensor<int32, [4]> attn_output_131_perm_0 = const()[name = tensor<string, []>("attn_output_131_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2024 = const()[name = tensor<string, []>("op_2024"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_50 = transpose(perm = attn_output_131_perm_0, x = attn_output_129_cast)[name = tensor<string, []>("transpose_50")];
            tensor<fp16, [1, 77, 1280]> input_345_cast = reshape(shape = var_2024, x = transpose_50)[name = tensor<string, []>("input_345_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545094976))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546733440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546734016)))];
            tensor<fp16, [1, 77, 1280]> linear_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized, x = input_345_cast)[name = tensor<string, []>("linear_129_cast")];
            tensor<fp16, [1, 77, 1280]> input_347_cast = add(x = input_339_cast, y = linear_129_cast)[name = tensor<string, []>("input_347_cast")];
            tensor<int32, [1]> input_349_axes_0 = const()[name = tensor<string, []>("input_349_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546736640)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546739264)))];
            tensor<fp16, [1, 77, 1280]> input_349_cast = layer_norm(axes = input_349_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_347_cast)[name = tensor<string, []>("input_349_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546741888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553295552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553296128))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553301312))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_130_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized, x = input_349_cast)[name = tensor<string, []>("linear_130_cast")];
            tensor<string, []> input_353_mode_0 = const()[name = tensor<string, []>("input_353_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_353_cast = gelu(mode = input_353_mode_0, x = linear_130_cast)[name = tensor<string, []>("input_353_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553301888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559855552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559856128)))];
            tensor<fp16, [1, 77, 1280]> linear_131_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized, x = input_353_cast)[name = tensor<string, []>("linear_131_cast")];
            tensor<fp16, [1, 77, 1280]> input_355_cast = add(x = input_347_cast, y = linear_131_cast)[name = tensor<string, []>("input_355_cast")];
            tensor<int32, [1]> hidden_states_133_axes_0 = const()[name = tensor<string, []>("hidden_states_133_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559858752)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559861376)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_133_cast = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_355_cast)[name = tensor<string, []>("hidden_states_133_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559864000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561502464))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561503040)))];
            tensor<fp16, [1, 77, 1280]> linear_132_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_132_cast")];
            tensor<fp16, []> var_2063_to_fp16 = const()[name = tensor<string, []>("op_2063_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_137_cast = mul(x = linear_132_cast, y = var_2063_to_fp16)[name = tensor<string, []>("tensor_137_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561505664))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563144128))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563144704)))];
            tensor<fp16, [1, 77, 1280]> linear_133_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_133_cast")];
            tensor<int32, [4]> var_2068 = const()[name = tensor<string, []>("op_2068"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2069_cast = reshape(shape = var_2068, x = linear_133_cast)[name = tensor<string, []>("op_2069_cast")];
            tensor<int32, [4]> var_2070_perm_0 = const()[name = tensor<string, []>("op_2070_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563147328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564785792))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564786368)))];
            tensor<fp16, [1, 77, 1280]> linear_134_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_134_cast")];
            tensor<int32, [4]> var_2075 = const()[name = tensor<string, []>("op_2075"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2076_cast = reshape(shape = var_2075, x = linear_134_cast)[name = tensor<string, []>("op_2076_cast")];
            tensor<int32, [4]> var_2077_perm_0 = const()[name = tensor<string, []>("op_2077_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2084 = const()[name = tensor<string, []>("op_2084"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2085_cast = reshape(shape = var_2084, x = tensor_137_cast)[name = tensor<string, []>("op_2085_cast")];
            tensor<int32, [4]> var_2086_perm_0 = const()[name = tensor<string, []>("op_2086_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2088 = const()[name = tensor<string, []>("op_2088"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_49 = transpose(perm = var_2086_perm_0, x = var_2085_cast)[name = tensor<string, []>("transpose_49")];
            tensor<fp16, [20, 77, 64]> query_states_45_cast = reshape(shape = var_2088, x = transpose_49)[name = tensor<string, []>("query_states_45_cast")];
            tensor<int32, [3]> var_2090 = const()[name = tensor<string, []>("op_2090"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_48 = transpose(perm = var_2070_perm_0, x = var_2069_cast)[name = tensor<string, []>("transpose_48")];
            tensor<fp16, [20, 77, 64]> key_states_91_cast = reshape(shape = var_2090, x = transpose_48)[name = tensor<string, []>("key_states_91_cast")];
            tensor<int32, [3]> var_2092 = const()[name = tensor<string, []>("op_2092"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_47 = transpose(perm = var_2077_perm_0, x = var_2076_cast)[name = tensor<string, []>("transpose_47")];
            tensor<fp16, [20, 77, 64]> value_states_91_cast = reshape(shape = var_2092, x = transpose_47)[name = tensor<string, []>("value_states_91_cast")];
            tensor<int32, [3]> var_2095_perm_0 = const()[name = tensor<string, []>("op_2095_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_133_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_133_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_46 = transpose(perm = var_2095_perm_0, x = key_states_91_cast)[name = tensor<string, []>("transpose_46")];
            tensor<fp16, [20, 77, 77]> attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = query_states_45_cast, y = transpose_46)[name = tensor<string, []>("attn_weights_133_cast")];
            tensor<int32, [4]> var_2097 = const()[name = tensor<string, []>("op_2097"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2098_cast = reshape(shape = var_2097, x = attn_weights_133_cast)[name = tensor<string, []>("op_2098_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_135_cast = add(x = var_2098_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_135_cast")];
            tensor<int32, [3]> var_2103 = const()[name = tensor<string, []>("op_2103"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_357_cast = reshape(shape = var_2103, x = attn_weights_135_cast)[name = tensor<string, []>("input_357_cast")];
            tensor<fp16, [20, 77, 77]> input_359_cast = softmax(axis = var_5, x = input_357_cast)[name = tensor<string, []>("input_359_cast")];
            tensor<bool, []> attn_output_133_transpose_x_0 = const()[name = tensor<string, []>("attn_output_133_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_133_transpose_y_0 = const()[name = tensor<string, []>("attn_output_133_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_133_cast = matmul(transpose_x = attn_output_133_transpose_x_0, transpose_y = attn_output_133_transpose_y_0, x = input_359_cast, y = value_states_91_cast)[name = tensor<string, []>("attn_output_133_cast")];
            tensor<int32, [4]> var_2108 = const()[name = tensor<string, []>("op_2108"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_135_cast = reshape(shape = var_2108, x = attn_output_133_cast)[name = tensor<string, []>("attn_output_135_cast")];
            tensor<int32, [4]> attn_output_137_perm_0 = const()[name = tensor<string, []>("attn_output_137_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2111 = const()[name = tensor<string, []>("op_2111"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_45 = transpose(perm = attn_output_137_perm_0, x = attn_output_135_cast)[name = tensor<string, []>("transpose_45")];
            tensor<fp16, [1, 77, 1280]> input_361_cast = reshape(shape = var_2111, x = transpose_45)[name = tensor<string, []>("input_361_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564788992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566427456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566428032)))];
            tensor<fp16, [1, 77, 1280]> linear_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized, x = input_361_cast)[name = tensor<string, []>("linear_135_cast")];
            tensor<fp16, [1, 77, 1280]> input_363_cast = add(x = input_355_cast, y = linear_135_cast)[name = tensor<string, []>("input_363_cast")];
            tensor<int32, [1]> input_365_axes_0 = const()[name = tensor<string, []>("input_365_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566430656)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566433280)))];
            tensor<fp16, [1, 77, 1280]> input_365_cast = layer_norm(axes = input_365_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_363_cast)[name = tensor<string, []>("input_365_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566435904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572989568))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572990144))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572995328))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_136_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized, x = input_365_cast)[name = tensor<string, []>("linear_136_cast")];
            tensor<string, []> input_369_mode_0 = const()[name = tensor<string, []>("input_369_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_369_cast = gelu(mode = input_369_mode_0, x = linear_136_cast)[name = tensor<string, []>("input_369_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(572995904))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579549568))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579550144)))];
            tensor<fp16, [1, 77, 1280]> linear_137_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized, x = input_369_cast)[name = tensor<string, []>("linear_137_cast")];
            tensor<fp16, [1, 77, 1280]> input_371_cast = add(x = input_363_cast, y = linear_137_cast)[name = tensor<string, []>("input_371_cast")];
            tensor<int32, [1]> hidden_states_139_axes_0 = const()[name = tensor<string, []>("hidden_states_139_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579552768)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579555392)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_139_cast = layer_norm(axes = hidden_states_139_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16, x = input_371_cast)[name = tensor<string, []>("hidden_states_139_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579558016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581196480))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581197056)))];
            tensor<fp16, [1, 77, 1280]> linear_138_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_139_cast)[name = tensor<string, []>("linear_138_cast")];
            tensor<fp16, []> var_2150_to_fp16 = const()[name = tensor<string, []>("op_2150_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_143_cast = mul(x = linear_138_cast, y = var_2150_to_fp16)[name = tensor<string, []>("tensor_143_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581199680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582838144))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582838720)))];
            tensor<fp16, [1, 77, 1280]> linear_139_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_139_cast)[name = tensor<string, []>("linear_139_cast")];
            tensor<int32, [4]> var_2155 = const()[name = tensor<string, []>("op_2155"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2156_cast = reshape(shape = var_2155, x = linear_139_cast)[name = tensor<string, []>("op_2156_cast")];
            tensor<int32, [4]> var_2157_perm_0 = const()[name = tensor<string, []>("op_2157_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582841344))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584479808))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584480384)))];
            tensor<fp16, [1, 77, 1280]> linear_140_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_139_cast)[name = tensor<string, []>("linear_140_cast")];
            tensor<int32, [4]> var_2162 = const()[name = tensor<string, []>("op_2162"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2163_cast = reshape(shape = var_2162, x = linear_140_cast)[name = tensor<string, []>("op_2163_cast")];
            tensor<int32, [4]> var_2164_perm_0 = const()[name = tensor<string, []>("op_2164_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2171 = const()[name = tensor<string, []>("op_2171"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2172_cast = reshape(shape = var_2171, x = tensor_143_cast)[name = tensor<string, []>("op_2172_cast")];
            tensor<int32, [4]> var_2173_perm_0 = const()[name = tensor<string, []>("op_2173_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2175 = const()[name = tensor<string, []>("op_2175"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_44 = transpose(perm = var_2173_perm_0, x = var_2172_cast)[name = tensor<string, []>("transpose_44")];
            tensor<fp16, [20, 77, 64]> query_states_47_cast = reshape(shape = var_2175, x = transpose_44)[name = tensor<string, []>("query_states_47_cast")];
            tensor<int32, [3]> var_2177 = const()[name = tensor<string, []>("op_2177"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_43 = transpose(perm = var_2157_perm_0, x = var_2156_cast)[name = tensor<string, []>("transpose_43")];
            tensor<fp16, [20, 77, 64]> key_states_95_cast = reshape(shape = var_2177, x = transpose_43)[name = tensor<string, []>("key_states_95_cast")];
            tensor<int32, [3]> var_2179 = const()[name = tensor<string, []>("op_2179"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_42 = transpose(perm = var_2164_perm_0, x = var_2163_cast)[name = tensor<string, []>("transpose_42")];
            tensor<fp16, [20, 77, 64]> value_states_95_cast = reshape(shape = var_2179, x = transpose_42)[name = tensor<string, []>("value_states_95_cast")];
            tensor<int32, [3]> var_2182_perm_0 = const()[name = tensor<string, []>("op_2182_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_139_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_139_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_139_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_139_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_41 = transpose(perm = var_2182_perm_0, x = key_states_95_cast)[name = tensor<string, []>("transpose_41")];
            tensor<fp16, [20, 77, 77]> attn_weights_139_cast = matmul(transpose_x = attn_weights_139_transpose_x_0, transpose_y = attn_weights_139_transpose_y_0, x = query_states_47_cast, y = transpose_41)[name = tensor<string, []>("attn_weights_139_cast")];
            tensor<int32, [4]> var_2184 = const()[name = tensor<string, []>("op_2184"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2185_cast = reshape(shape = var_2184, x = attn_weights_139_cast)[name = tensor<string, []>("op_2185_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_141_cast = add(x = var_2185_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_141_cast")];
            tensor<int32, [3]> var_2190 = const()[name = tensor<string, []>("op_2190"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_373_cast = reshape(shape = var_2190, x = attn_weights_141_cast)[name = tensor<string, []>("input_373_cast")];
            tensor<fp16, [20, 77, 77]> input_375_cast = softmax(axis = var_5, x = input_373_cast)[name = tensor<string, []>("input_375_cast")];
            tensor<bool, []> attn_output_139_transpose_x_0 = const()[name = tensor<string, []>("attn_output_139_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_139_transpose_y_0 = const()[name = tensor<string, []>("attn_output_139_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_139_cast = matmul(transpose_x = attn_output_139_transpose_x_0, transpose_y = attn_output_139_transpose_y_0, x = input_375_cast, y = value_states_95_cast)[name = tensor<string, []>("attn_output_139_cast")];
            tensor<int32, [4]> var_2195 = const()[name = tensor<string, []>("op_2195"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_141_cast = reshape(shape = var_2195, x = attn_output_139_cast)[name = tensor<string, []>("attn_output_141_cast")];
            tensor<int32, [4]> attn_output_143_perm_0 = const()[name = tensor<string, []>("attn_output_143_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2198 = const()[name = tensor<string, []>("op_2198"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_40 = transpose(perm = attn_output_143_perm_0, x = attn_output_141_cast)[name = tensor<string, []>("transpose_40")];
            tensor<fp16, [1, 77, 1280]> input_377_cast = reshape(shape = var_2198, x = transpose_40)[name = tensor<string, []>("input_377_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(584483008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586121472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586122048)))];
            tensor<fp16, [1, 77, 1280]> linear_141_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16_palettized, x = input_377_cast)[name = tensor<string, []>("linear_141_cast")];
            tensor<fp16, [1, 77, 1280]> input_379_cast = add(x = input_371_cast, y = linear_141_cast)[name = tensor<string, []>("input_379_cast")];
            tensor<int32, [1]> input_381_axes_0 = const()[name = tensor<string, []>("input_381_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586124672)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586127296)))];
            tensor<fp16, [1, 77, 1280]> input_381_cast = layer_norm(axes = input_381_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16, x = input_379_cast)[name = tensor<string, []>("input_381_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586129920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592683584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592684160))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592689344))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_142_cast = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16_palettized, x = input_381_cast)[name = tensor<string, []>("linear_142_cast")];
            tensor<string, []> input_385_mode_0 = const()[name = tensor<string, []>("input_385_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_385_cast = gelu(mode = input_385_mode_0, x = linear_142_cast)[name = tensor<string, []>("input_385_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592689920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599243584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599244160)))];
            tensor<fp16, [1, 77, 1280]> linear_143_cast = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16_palettized, x = input_385_cast)[name = tensor<string, []>("linear_143_cast")];
            tensor<fp16, [1, 77, 1280]> input_387_cast = add(x = input_379_cast, y = linear_143_cast)[name = tensor<string, []>("input_387_cast")];
            tensor<int32, [1]> hidden_states_145_axes_0 = const()[name = tensor<string, []>("hidden_states_145_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599246784)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599249408)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_145_cast = layer_norm(axes = hidden_states_145_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16, x = input_387_cast)[name = tensor<string, []>("hidden_states_145_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599252032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600890496))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600891072)))];
            tensor<fp16, [1, 77, 1280]> linear_144_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_145_cast)[name = tensor<string, []>("linear_144_cast")];
            tensor<fp16, []> var_2237_to_fp16 = const()[name = tensor<string, []>("op_2237_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_149_cast = mul(x = linear_144_cast, y = var_2237_to_fp16)[name = tensor<string, []>("tensor_149_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600893696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602532160))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602532736)))];
            tensor<fp16, [1, 77, 1280]> linear_145_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_145_cast)[name = tensor<string, []>("linear_145_cast")];
            tensor<int32, [4]> var_2242 = const()[name = tensor<string, []>("op_2242"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2243_cast = reshape(shape = var_2242, x = linear_145_cast)[name = tensor<string, []>("op_2243_cast")];
            tensor<int32, [4]> var_2244_perm_0 = const()[name = tensor<string, []>("op_2244_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602535360))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604173824))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604174400)))];
            tensor<fp16, [1, 77, 1280]> linear_146_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_145_cast)[name = tensor<string, []>("linear_146_cast")];
            tensor<int32, [4]> var_2249 = const()[name = tensor<string, []>("op_2249"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2250_cast = reshape(shape = var_2249, x = linear_146_cast)[name = tensor<string, []>("op_2250_cast")];
            tensor<int32, [4]> var_2251_perm_0 = const()[name = tensor<string, []>("op_2251_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2258 = const()[name = tensor<string, []>("op_2258"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2259_cast = reshape(shape = var_2258, x = tensor_149_cast)[name = tensor<string, []>("op_2259_cast")];
            tensor<int32, [4]> var_2260_perm_0 = const()[name = tensor<string, []>("op_2260_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2262 = const()[name = tensor<string, []>("op_2262"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_39 = transpose(perm = var_2260_perm_0, x = var_2259_cast)[name = tensor<string, []>("transpose_39")];
            tensor<fp16, [20, 77, 64]> query_states_49_cast = reshape(shape = var_2262, x = transpose_39)[name = tensor<string, []>("query_states_49_cast")];
            tensor<int32, [3]> var_2264 = const()[name = tensor<string, []>("op_2264"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_38 = transpose(perm = var_2244_perm_0, x = var_2243_cast)[name = tensor<string, []>("transpose_38")];
            tensor<fp16, [20, 77, 64]> key_states_99_cast = reshape(shape = var_2264, x = transpose_38)[name = tensor<string, []>("key_states_99_cast")];
            tensor<int32, [3]> var_2266 = const()[name = tensor<string, []>("op_2266"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_37 = transpose(perm = var_2251_perm_0, x = var_2250_cast)[name = tensor<string, []>("transpose_37")];
            tensor<fp16, [20, 77, 64]> value_states_99_cast = reshape(shape = var_2266, x = transpose_37)[name = tensor<string, []>("value_states_99_cast")];
            tensor<int32, [3]> var_2269_perm_0 = const()[name = tensor<string, []>("op_2269_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_145_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_145_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_145_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_145_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_36 = transpose(perm = var_2269_perm_0, x = key_states_99_cast)[name = tensor<string, []>("transpose_36")];
            tensor<fp16, [20, 77, 77]> attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = query_states_49_cast, y = transpose_36)[name = tensor<string, []>("attn_weights_145_cast")];
            tensor<int32, [4]> var_2271 = const()[name = tensor<string, []>("op_2271"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2272_cast = reshape(shape = var_2271, x = attn_weights_145_cast)[name = tensor<string, []>("op_2272_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_147_cast = add(x = var_2272_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_147_cast")];
            tensor<int32, [3]> var_2277 = const()[name = tensor<string, []>("op_2277"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_389_cast = reshape(shape = var_2277, x = attn_weights_147_cast)[name = tensor<string, []>("input_389_cast")];
            tensor<fp16, [20, 77, 77]> input_391_cast = softmax(axis = var_5, x = input_389_cast)[name = tensor<string, []>("input_391_cast")];
            tensor<bool, []> attn_output_145_transpose_x_0 = const()[name = tensor<string, []>("attn_output_145_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_145_transpose_y_0 = const()[name = tensor<string, []>("attn_output_145_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_145_cast = matmul(transpose_x = attn_output_145_transpose_x_0, transpose_y = attn_output_145_transpose_y_0, x = input_391_cast, y = value_states_99_cast)[name = tensor<string, []>("attn_output_145_cast")];
            tensor<int32, [4]> var_2282 = const()[name = tensor<string, []>("op_2282"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_147_cast = reshape(shape = var_2282, x = attn_output_145_cast)[name = tensor<string, []>("attn_output_147_cast")];
            tensor<int32, [4]> attn_output_149_perm_0 = const()[name = tensor<string, []>("attn_output_149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2285 = const()[name = tensor<string, []>("op_2285"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_35 = transpose(perm = attn_output_149_perm_0, x = attn_output_147_cast)[name = tensor<string, []>("transpose_35")];
            tensor<fp16, [1, 77, 1280]> input_393_cast = reshape(shape = var_2285, x = transpose_35)[name = tensor<string, []>("input_393_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604177024))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605815488))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605816064)))];
            tensor<fp16, [1, 77, 1280]> linear_147_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16_palettized, x = input_393_cast)[name = tensor<string, []>("linear_147_cast")];
            tensor<fp16, [1, 77, 1280]> input_395_cast = add(x = input_387_cast, y = linear_147_cast)[name = tensor<string, []>("input_395_cast")];
            tensor<int32, [1]> input_397_axes_0 = const()[name = tensor<string, []>("input_397_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605818688)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605821312)))];
            tensor<fp16, [1, 77, 1280]> input_397_cast = layer_norm(axes = input_397_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16, x = input_395_cast)[name = tensor<string, []>("input_397_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605823936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612377600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612378176))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612383360))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_148_cast = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16_palettized, x = input_397_cast)[name = tensor<string, []>("linear_148_cast")];
            tensor<string, []> input_401_mode_0 = const()[name = tensor<string, []>("input_401_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_401_cast = gelu(mode = input_401_mode_0, x = linear_148_cast)[name = tensor<string, []>("input_401_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612383936))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618937600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618938176)))];
            tensor<fp16, [1, 77, 1280]> linear_149_cast = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16_palettized, x = input_401_cast)[name = tensor<string, []>("linear_149_cast")];
            tensor<fp16, [1, 77, 1280]> input_403_cast = add(x = input_395_cast, y = linear_149_cast)[name = tensor<string, []>("input_403_cast")];
            tensor<int32, [1]> hidden_states_151_axes_0 = const()[name = tensor<string, []>("hidden_states_151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618940800)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618943424)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_151_cast = layer_norm(axes = hidden_states_151_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16, x = input_403_cast)[name = tensor<string, []>("hidden_states_151_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618946048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620584512))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620585088)))];
            tensor<fp16, [1, 77, 1280]> linear_150_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_151_cast)[name = tensor<string, []>("linear_150_cast")];
            tensor<fp16, []> var_2324_to_fp16 = const()[name = tensor<string, []>("op_2324_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_155_cast = mul(x = linear_150_cast, y = var_2324_to_fp16)[name = tensor<string, []>("tensor_155_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620587712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622226176))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622226752)))];
            tensor<fp16, [1, 77, 1280]> linear_151_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_151_cast)[name = tensor<string, []>("linear_151_cast")];
            tensor<int32, [4]> var_2329 = const()[name = tensor<string, []>("op_2329"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2330_cast = reshape(shape = var_2329, x = linear_151_cast)[name = tensor<string, []>("op_2330_cast")];
            tensor<int32, [4]> var_2331_perm_0 = const()[name = tensor<string, []>("op_2331_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622229376))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623867840))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623868416)))];
            tensor<fp16, [1, 77, 1280]> linear_152_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_151_cast)[name = tensor<string, []>("linear_152_cast")];
            tensor<int32, [4]> var_2336 = const()[name = tensor<string, []>("op_2336"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2337_cast = reshape(shape = var_2336, x = linear_152_cast)[name = tensor<string, []>("op_2337_cast")];
            tensor<int32, [4]> var_2338_perm_0 = const()[name = tensor<string, []>("op_2338_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2345 = const()[name = tensor<string, []>("op_2345"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2346_cast = reshape(shape = var_2345, x = tensor_155_cast)[name = tensor<string, []>("op_2346_cast")];
            tensor<int32, [4]> var_2347_perm_0 = const()[name = tensor<string, []>("op_2347_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2349 = const()[name = tensor<string, []>("op_2349"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_34 = transpose(perm = var_2347_perm_0, x = var_2346_cast)[name = tensor<string, []>("transpose_34")];
            tensor<fp16, [20, 77, 64]> query_states_51_cast = reshape(shape = var_2349, x = transpose_34)[name = tensor<string, []>("query_states_51_cast")];
            tensor<int32, [3]> var_2351 = const()[name = tensor<string, []>("op_2351"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_33 = transpose(perm = var_2331_perm_0, x = var_2330_cast)[name = tensor<string, []>("transpose_33")];
            tensor<fp16, [20, 77, 64]> key_states_103_cast = reshape(shape = var_2351, x = transpose_33)[name = tensor<string, []>("key_states_103_cast")];
            tensor<int32, [3]> var_2353 = const()[name = tensor<string, []>("op_2353"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_32 = transpose(perm = var_2338_perm_0, x = var_2337_cast)[name = tensor<string, []>("transpose_32")];
            tensor<fp16, [20, 77, 64]> value_states_103_cast = reshape(shape = var_2353, x = transpose_32)[name = tensor<string, []>("value_states_103_cast")];
            tensor<int32, [3]> var_2356_perm_0 = const()[name = tensor<string, []>("op_2356_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_151_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_151_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_151_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_151_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_31 = transpose(perm = var_2356_perm_0, x = key_states_103_cast)[name = tensor<string, []>("transpose_31")];
            tensor<fp16, [20, 77, 77]> attn_weights_151_cast = matmul(transpose_x = attn_weights_151_transpose_x_0, transpose_y = attn_weights_151_transpose_y_0, x = query_states_51_cast, y = transpose_31)[name = tensor<string, []>("attn_weights_151_cast")];
            tensor<int32, [4]> var_2358 = const()[name = tensor<string, []>("op_2358"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2359_cast = reshape(shape = var_2358, x = attn_weights_151_cast)[name = tensor<string, []>("op_2359_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_153_cast = add(x = var_2359_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_153_cast")];
            tensor<int32, [3]> var_2364 = const()[name = tensor<string, []>("op_2364"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_405_cast = reshape(shape = var_2364, x = attn_weights_153_cast)[name = tensor<string, []>("input_405_cast")];
            tensor<fp16, [20, 77, 77]> input_407_cast = softmax(axis = var_5, x = input_405_cast)[name = tensor<string, []>("input_407_cast")];
            tensor<bool, []> attn_output_151_transpose_x_0 = const()[name = tensor<string, []>("attn_output_151_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_151_transpose_y_0 = const()[name = tensor<string, []>("attn_output_151_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_151_cast = matmul(transpose_x = attn_output_151_transpose_x_0, transpose_y = attn_output_151_transpose_y_0, x = input_407_cast, y = value_states_103_cast)[name = tensor<string, []>("attn_output_151_cast")];
            tensor<int32, [4]> var_2369 = const()[name = tensor<string, []>("op_2369"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_153_cast = reshape(shape = var_2369, x = attn_output_151_cast)[name = tensor<string, []>("attn_output_153_cast")];
            tensor<int32, [4]> attn_output_155_perm_0 = const()[name = tensor<string, []>("attn_output_155_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2372 = const()[name = tensor<string, []>("op_2372"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_30 = transpose(perm = attn_output_155_perm_0, x = attn_output_153_cast)[name = tensor<string, []>("transpose_30")];
            tensor<fp16, [1, 77, 1280]> input_409_cast = reshape(shape = var_2372, x = transpose_30)[name = tensor<string, []>("input_409_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(623871040))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625509504))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625510080)))];
            tensor<fp16, [1, 77, 1280]> linear_153_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16_palettized, x = input_409_cast)[name = tensor<string, []>("linear_153_cast")];
            tensor<fp16, [1, 77, 1280]> input_411_cast = add(x = input_403_cast, y = linear_153_cast)[name = tensor<string, []>("input_411_cast")];
            tensor<int32, [1]> input_413_axes_0 = const()[name = tensor<string, []>("input_413_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625512704)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625515328)))];
            tensor<fp16, [1, 77, 1280]> input_413_cast = layer_norm(axes = input_413_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16, x = input_411_cast)[name = tensor<string, []>("input_413_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625517952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632071616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632072192))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632077376))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_154_cast = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16_palettized, x = input_413_cast)[name = tensor<string, []>("linear_154_cast")];
            tensor<string, []> input_417_mode_0 = const()[name = tensor<string, []>("input_417_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_417_cast = gelu(mode = input_417_mode_0, x = linear_154_cast)[name = tensor<string, []>("input_417_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632077952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638631616))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638632192)))];
            tensor<fp16, [1, 77, 1280]> linear_155_cast = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16_palettized, x = input_417_cast)[name = tensor<string, []>("linear_155_cast")];
            tensor<fp16, [1, 77, 1280]> input_419_cast = add(x = input_411_cast, y = linear_155_cast)[name = tensor<string, []>("input_419_cast")];
            tensor<int32, [1]> hidden_states_157_axes_0 = const()[name = tensor<string, []>("hidden_states_157_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638634816)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638637440)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_157_cast = layer_norm(axes = hidden_states_157_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16, x = input_419_cast)[name = tensor<string, []>("hidden_states_157_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638640064))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640278528))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640279104)))];
            tensor<fp16, [1, 77, 1280]> linear_156_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_157_cast)[name = tensor<string, []>("linear_156_cast")];
            tensor<fp16, []> var_2411_to_fp16 = const()[name = tensor<string, []>("op_2411_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_161_cast = mul(x = linear_156_cast, y = var_2411_to_fp16)[name = tensor<string, []>("tensor_161_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640281728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641920192))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641920768)))];
            tensor<fp16, [1, 77, 1280]> linear_157_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_157_cast)[name = tensor<string, []>("linear_157_cast")];
            tensor<int32, [4]> var_2416 = const()[name = tensor<string, []>("op_2416"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2417_cast = reshape(shape = var_2416, x = linear_157_cast)[name = tensor<string, []>("op_2417_cast")];
            tensor<int32, [4]> var_2418_perm_0 = const()[name = tensor<string, []>("op_2418_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641923392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643561856))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643562432)))];
            tensor<fp16, [1, 77, 1280]> linear_158_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_157_cast)[name = tensor<string, []>("linear_158_cast")];
            tensor<int32, [4]> var_2423 = const()[name = tensor<string, []>("op_2423"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2424_cast = reshape(shape = var_2423, x = linear_158_cast)[name = tensor<string, []>("op_2424_cast")];
            tensor<int32, [4]> var_2425_perm_0 = const()[name = tensor<string, []>("op_2425_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2432 = const()[name = tensor<string, []>("op_2432"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2433_cast = reshape(shape = var_2432, x = tensor_161_cast)[name = tensor<string, []>("op_2433_cast")];
            tensor<int32, [4]> var_2434_perm_0 = const()[name = tensor<string, []>("op_2434_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2436 = const()[name = tensor<string, []>("op_2436"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_29 = transpose(perm = var_2434_perm_0, x = var_2433_cast)[name = tensor<string, []>("transpose_29")];
            tensor<fp16, [20, 77, 64]> query_states_53_cast = reshape(shape = var_2436, x = transpose_29)[name = tensor<string, []>("query_states_53_cast")];
            tensor<int32, [3]> var_2438 = const()[name = tensor<string, []>("op_2438"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_28 = transpose(perm = var_2418_perm_0, x = var_2417_cast)[name = tensor<string, []>("transpose_28")];
            tensor<fp16, [20, 77, 64]> key_states_107_cast = reshape(shape = var_2438, x = transpose_28)[name = tensor<string, []>("key_states_107_cast")];
            tensor<int32, [3]> var_2440 = const()[name = tensor<string, []>("op_2440"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_27 = transpose(perm = var_2425_perm_0, x = var_2424_cast)[name = tensor<string, []>("transpose_27")];
            tensor<fp16, [20, 77, 64]> value_states_107_cast = reshape(shape = var_2440, x = transpose_27)[name = tensor<string, []>("value_states_107_cast")];
            tensor<int32, [3]> var_2443_perm_0 = const()[name = tensor<string, []>("op_2443_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_157_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_157_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_157_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_157_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_26 = transpose(perm = var_2443_perm_0, x = key_states_107_cast)[name = tensor<string, []>("transpose_26")];
            tensor<fp16, [20, 77, 77]> attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = query_states_53_cast, y = transpose_26)[name = tensor<string, []>("attn_weights_157_cast")];
            tensor<int32, [4]> var_2445 = const()[name = tensor<string, []>("op_2445"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2446_cast = reshape(shape = var_2445, x = attn_weights_157_cast)[name = tensor<string, []>("op_2446_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_159_cast = add(x = var_2446_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_159_cast")];
            tensor<int32, [3]> var_2451 = const()[name = tensor<string, []>("op_2451"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_421_cast = reshape(shape = var_2451, x = attn_weights_159_cast)[name = tensor<string, []>("input_421_cast")];
            tensor<fp16, [20, 77, 77]> input_423_cast = softmax(axis = var_5, x = input_421_cast)[name = tensor<string, []>("input_423_cast")];
            tensor<bool, []> attn_output_157_transpose_x_0 = const()[name = tensor<string, []>("attn_output_157_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_157_transpose_y_0 = const()[name = tensor<string, []>("attn_output_157_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_157_cast = matmul(transpose_x = attn_output_157_transpose_x_0, transpose_y = attn_output_157_transpose_y_0, x = input_423_cast, y = value_states_107_cast)[name = tensor<string, []>("attn_output_157_cast")];
            tensor<int32, [4]> var_2456 = const()[name = tensor<string, []>("op_2456"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_159_cast = reshape(shape = var_2456, x = attn_output_157_cast)[name = tensor<string, []>("attn_output_159_cast")];
            tensor<int32, [4]> attn_output_161_perm_0 = const()[name = tensor<string, []>("attn_output_161_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2459 = const()[name = tensor<string, []>("op_2459"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_25 = transpose(perm = attn_output_161_perm_0, x = attn_output_159_cast)[name = tensor<string, []>("transpose_25")];
            tensor<fp16, [1, 77, 1280]> input_425_cast = reshape(shape = var_2459, x = transpose_25)[name = tensor<string, []>("input_425_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643565056))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645203520))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645204096)))];
            tensor<fp16, [1, 77, 1280]> linear_159_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16_palettized, x = input_425_cast)[name = tensor<string, []>("linear_159_cast")];
            tensor<fp16, [1, 77, 1280]> input_427_cast = add(x = input_419_cast, y = linear_159_cast)[name = tensor<string, []>("input_427_cast")];
            tensor<int32, [1]> input_429_axes_0 = const()[name = tensor<string, []>("input_429_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645206720)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645209344)))];
            tensor<fp16, [1, 77, 1280]> input_429_cast = layer_norm(axes = input_429_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16, x = input_427_cast)[name = tensor<string, []>("input_429_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645211968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651765632))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651766208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651771392))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_160_cast = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16_palettized, x = input_429_cast)[name = tensor<string, []>("linear_160_cast")];
            tensor<string, []> input_433_mode_0 = const()[name = tensor<string, []>("input_433_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_433_cast = gelu(mode = input_433_mode_0, x = linear_160_cast)[name = tensor<string, []>("input_433_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651771968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658325632))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658326208)))];
            tensor<fp16, [1, 77, 1280]> linear_161_cast = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16_palettized, x = input_433_cast)[name = tensor<string, []>("linear_161_cast")];
            tensor<fp16, [1, 77, 1280]> input_435_cast = add(x = input_427_cast, y = linear_161_cast)[name = tensor<string, []>("input_435_cast")];
            tensor<int32, [1]> hidden_states_163_axes_0 = const()[name = tensor<string, []>("hidden_states_163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658328832)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658331456)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_163_cast = layer_norm(axes = hidden_states_163_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16, x = input_435_cast)[name = tensor<string, []>("hidden_states_163_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658334080))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(659972544))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(659973120)))];
            tensor<fp16, [1, 77, 1280]> linear_162_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_163_cast)[name = tensor<string, []>("linear_162_cast")];
            tensor<fp16, []> var_2498_to_fp16 = const()[name = tensor<string, []>("op_2498_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_167_cast = mul(x = linear_162_cast, y = var_2498_to_fp16)[name = tensor<string, []>("tensor_167_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(659975744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661614208))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661614784)))];
            tensor<fp16, [1, 77, 1280]> linear_163_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_163_cast)[name = tensor<string, []>("linear_163_cast")];
            tensor<int32, [4]> var_2503 = const()[name = tensor<string, []>("op_2503"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2504_cast = reshape(shape = var_2503, x = linear_163_cast)[name = tensor<string, []>("op_2504_cast")];
            tensor<int32, [4]> var_2505_perm_0 = const()[name = tensor<string, []>("op_2505_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661617408))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663255872))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663256448)))];
            tensor<fp16, [1, 77, 1280]> linear_164_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_163_cast)[name = tensor<string, []>("linear_164_cast")];
            tensor<int32, [4]> var_2510 = const()[name = tensor<string, []>("op_2510"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2511_cast = reshape(shape = var_2510, x = linear_164_cast)[name = tensor<string, []>("op_2511_cast")];
            tensor<int32, [4]> var_2512_perm_0 = const()[name = tensor<string, []>("op_2512_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2519 = const()[name = tensor<string, []>("op_2519"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2520_cast = reshape(shape = var_2519, x = tensor_167_cast)[name = tensor<string, []>("op_2520_cast")];
            tensor<int32, [4]> var_2521_perm_0 = const()[name = tensor<string, []>("op_2521_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2523 = const()[name = tensor<string, []>("op_2523"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_24 = transpose(perm = var_2521_perm_0, x = var_2520_cast)[name = tensor<string, []>("transpose_24")];
            tensor<fp16, [20, 77, 64]> query_states_55_cast = reshape(shape = var_2523, x = transpose_24)[name = tensor<string, []>("query_states_55_cast")];
            tensor<int32, [3]> var_2525 = const()[name = tensor<string, []>("op_2525"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_23 = transpose(perm = var_2505_perm_0, x = var_2504_cast)[name = tensor<string, []>("transpose_23")];
            tensor<fp16, [20, 77, 64]> key_states_111_cast = reshape(shape = var_2525, x = transpose_23)[name = tensor<string, []>("key_states_111_cast")];
            tensor<int32, [3]> var_2527 = const()[name = tensor<string, []>("op_2527"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_22 = transpose(perm = var_2512_perm_0, x = var_2511_cast)[name = tensor<string, []>("transpose_22")];
            tensor<fp16, [20, 77, 64]> value_states_111_cast = reshape(shape = var_2527, x = transpose_22)[name = tensor<string, []>("value_states_111_cast")];
            tensor<int32, [3]> var_2530_perm_0 = const()[name = tensor<string, []>("op_2530_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_163_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_163_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_163_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_163_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_21 = transpose(perm = var_2530_perm_0, x = key_states_111_cast)[name = tensor<string, []>("transpose_21")];
            tensor<fp16, [20, 77, 77]> attn_weights_163_cast = matmul(transpose_x = attn_weights_163_transpose_x_0, transpose_y = attn_weights_163_transpose_y_0, x = query_states_55_cast, y = transpose_21)[name = tensor<string, []>("attn_weights_163_cast")];
            tensor<int32, [4]> var_2532 = const()[name = tensor<string, []>("op_2532"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2533_cast = reshape(shape = var_2532, x = attn_weights_163_cast)[name = tensor<string, []>("op_2533_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_165_cast = add(x = var_2533_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_165_cast")];
            tensor<int32, [3]> var_2538 = const()[name = tensor<string, []>("op_2538"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_437_cast = reshape(shape = var_2538, x = attn_weights_165_cast)[name = tensor<string, []>("input_437_cast")];
            tensor<fp16, [20, 77, 77]> input_439_cast = softmax(axis = var_5, x = input_437_cast)[name = tensor<string, []>("input_439_cast")];
            tensor<bool, []> attn_output_163_transpose_x_0 = const()[name = tensor<string, []>("attn_output_163_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_163_transpose_y_0 = const()[name = tensor<string, []>("attn_output_163_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_163_cast = matmul(transpose_x = attn_output_163_transpose_x_0, transpose_y = attn_output_163_transpose_y_0, x = input_439_cast, y = value_states_111_cast)[name = tensor<string, []>("attn_output_163_cast")];
            tensor<int32, [4]> var_2543 = const()[name = tensor<string, []>("op_2543"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_165_cast = reshape(shape = var_2543, x = attn_output_163_cast)[name = tensor<string, []>("attn_output_165_cast")];
            tensor<int32, [4]> attn_output_167_perm_0 = const()[name = tensor<string, []>("attn_output_167_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2546 = const()[name = tensor<string, []>("op_2546"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_20 = transpose(perm = attn_output_167_perm_0, x = attn_output_165_cast)[name = tensor<string, []>("transpose_20")];
            tensor<fp16, [1, 77, 1280]> input_441_cast = reshape(shape = var_2546, x = transpose_20)[name = tensor<string, []>("input_441_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(663259072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664897536))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664898112)))];
            tensor<fp16, [1, 77, 1280]> linear_165_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16_palettized, x = input_441_cast)[name = tensor<string, []>("linear_165_cast")];
            tensor<fp16, [1, 77, 1280]> input_443_cast = add(x = input_435_cast, y = linear_165_cast)[name = tensor<string, []>("input_443_cast")];
            tensor<int32, [1]> input_445_axes_0 = const()[name = tensor<string, []>("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664900736)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664903360)))];
            tensor<fp16, [1, 77, 1280]> input_445_cast = layer_norm(axes = input_445_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16, x = input_443_cast)[name = tensor<string, []>("input_445_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664905984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671459648))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671460224))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671465408))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_166_cast = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16_palettized, x = input_445_cast)[name = tensor<string, []>("linear_166_cast")];
            tensor<string, []> input_449_mode_0 = const()[name = tensor<string, []>("input_449_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_449_cast = gelu(mode = input_449_mode_0, x = linear_166_cast)[name = tensor<string, []>("input_449_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671465984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678019648))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678020224)))];
            tensor<fp16, [1, 77, 1280]> linear_167_cast = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16_palettized, x = input_449_cast)[name = tensor<string, []>("linear_167_cast")];
            tensor<fp16, [1, 77, 1280]> input_451_cast = add(x = input_443_cast, y = linear_167_cast)[name = tensor<string, []>("input_451_cast")];
            tensor<int32, [1]> hidden_states_169_axes_0 = const()[name = tensor<string, []>("hidden_states_169_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678022848)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678025472)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_169_cast = layer_norm(axes = hidden_states_169_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16, x = input_451_cast)[name = tensor<string, []>("hidden_states_169_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678028096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679666560))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679667136)))];
            tensor<fp16, [1, 77, 1280]> linear_168_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor<string, []>("linear_168_cast")];
            tensor<fp16, []> var_2585_to_fp16 = const()[name = tensor<string, []>("op_2585_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_173_cast = mul(x = linear_168_cast, y = var_2585_to_fp16)[name = tensor<string, []>("tensor_173_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(679669760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681308224))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681308800)))];
            tensor<fp16, [1, 77, 1280]> linear_169_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor<string, []>("linear_169_cast")];
            tensor<int32, [4]> var_2590 = const()[name = tensor<string, []>("op_2590"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2591_cast = reshape(shape = var_2590, x = linear_169_cast)[name = tensor<string, []>("op_2591_cast")];
            tensor<int32, [4]> var_2592_perm_0 = const()[name = tensor<string, []>("op_2592_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681311424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(682949888))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(682950464)))];
            tensor<fp16, [1, 77, 1280]> linear_170_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor<string, []>("linear_170_cast")];
            tensor<int32, [4]> var_2597 = const()[name = tensor<string, []>("op_2597"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2598_cast = reshape(shape = var_2597, x = linear_170_cast)[name = tensor<string, []>("op_2598_cast")];
            tensor<int32, [4]> var_2599_perm_0 = const()[name = tensor<string, []>("op_2599_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2606 = const()[name = tensor<string, []>("op_2606"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2607_cast = reshape(shape = var_2606, x = tensor_173_cast)[name = tensor<string, []>("op_2607_cast")];
            tensor<int32, [4]> var_2608_perm_0 = const()[name = tensor<string, []>("op_2608_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2610 = const()[name = tensor<string, []>("op_2610"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_19 = transpose(perm = var_2608_perm_0, x = var_2607_cast)[name = tensor<string, []>("transpose_19")];
            tensor<fp16, [20, 77, 64]> query_states_57_cast = reshape(shape = var_2610, x = transpose_19)[name = tensor<string, []>("query_states_57_cast")];
            tensor<int32, [3]> var_2612 = const()[name = tensor<string, []>("op_2612"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_18 = transpose(perm = var_2592_perm_0, x = var_2591_cast)[name = tensor<string, []>("transpose_18")];
            tensor<fp16, [20, 77, 64]> key_states_115_cast = reshape(shape = var_2612, x = transpose_18)[name = tensor<string, []>("key_states_115_cast")];
            tensor<int32, [3]> var_2614 = const()[name = tensor<string, []>("op_2614"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_17 = transpose(perm = var_2599_perm_0, x = var_2598_cast)[name = tensor<string, []>("transpose_17")];
            tensor<fp16, [20, 77, 64]> value_states_115_cast = reshape(shape = var_2614, x = transpose_17)[name = tensor<string, []>("value_states_115_cast")];
            tensor<int32, [3]> var_2617_perm_0 = const()[name = tensor<string, []>("op_2617_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_169_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_169_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_169_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_169_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_16 = transpose(perm = var_2617_perm_0, x = key_states_115_cast)[name = tensor<string, []>("transpose_16")];
            tensor<fp16, [20, 77, 77]> attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = query_states_57_cast, y = transpose_16)[name = tensor<string, []>("attn_weights_169_cast")];
            tensor<int32, [4]> var_2619 = const()[name = tensor<string, []>("op_2619"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2620_cast = reshape(shape = var_2619, x = attn_weights_169_cast)[name = tensor<string, []>("op_2620_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_171_cast = add(x = var_2620_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_171_cast")];
            tensor<int32, [3]> var_2625 = const()[name = tensor<string, []>("op_2625"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_453_cast = reshape(shape = var_2625, x = attn_weights_171_cast)[name = tensor<string, []>("input_453_cast")];
            tensor<fp16, [20, 77, 77]> input_455_cast = softmax(axis = var_5, x = input_453_cast)[name = tensor<string, []>("input_455_cast")];
            tensor<bool, []> attn_output_169_transpose_x_0 = const()[name = tensor<string, []>("attn_output_169_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_169_transpose_y_0 = const()[name = tensor<string, []>("attn_output_169_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_169_cast = matmul(transpose_x = attn_output_169_transpose_x_0, transpose_y = attn_output_169_transpose_y_0, x = input_455_cast, y = value_states_115_cast)[name = tensor<string, []>("attn_output_169_cast")];
            tensor<int32, [4]> var_2630 = const()[name = tensor<string, []>("op_2630"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_171_cast = reshape(shape = var_2630, x = attn_output_169_cast)[name = tensor<string, []>("attn_output_171_cast")];
            tensor<int32, [4]> attn_output_173_perm_0 = const()[name = tensor<string, []>("attn_output_173_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2633 = const()[name = tensor<string, []>("op_2633"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_15 = transpose(perm = attn_output_173_perm_0, x = attn_output_171_cast)[name = tensor<string, []>("transpose_15")];
            tensor<fp16, [1, 77, 1280]> input_457_cast = reshape(shape = var_2633, x = transpose_15)[name = tensor<string, []>("input_457_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(682953088))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684591552))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684592128)))];
            tensor<fp16, [1, 77, 1280]> linear_171_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16_palettized, x = input_457_cast)[name = tensor<string, []>("linear_171_cast")];
            tensor<fp16, [1, 77, 1280]> input_459_cast = add(x = input_451_cast, y = linear_171_cast)[name = tensor<string, []>("input_459_cast")];
            tensor<int32, [1]> input_461_axes_0 = const()[name = tensor<string, []>("input_461_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684594752)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684597376)))];
            tensor<fp16, [1, 77, 1280]> input_461_cast = layer_norm(axes = input_461_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16, x = input_459_cast)[name = tensor<string, []>("input_461_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684600000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691153664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691154240))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691159424))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_172_cast = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16_palettized, x = input_461_cast)[name = tensor<string, []>("linear_172_cast")];
            tensor<string, []> input_465_mode_0 = const()[name = tensor<string, []>("input_465_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_465_cast = gelu(mode = input_465_mode_0, x = linear_172_cast)[name = tensor<string, []>("input_465_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691160000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697713664))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697714240)))];
            tensor<fp16, [1, 77, 1280]> linear_173_cast = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16_palettized, x = input_465_cast)[name = tensor<string, []>("linear_173_cast")];
            tensor<fp16, [1, 77, 1280]> input_467_cast = add(x = input_459_cast, y = linear_173_cast)[name = tensor<string, []>("input_467_cast")];
            tensor<int32, [1]> hidden_states_175_axes_0 = const()[name = tensor<string, []>("hidden_states_175_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697716864)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697719488)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_175_cast = layer_norm(axes = hidden_states_175_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16, x = input_467_cast)[name = tensor<string, []>("hidden_states_175_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(697722112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699360576))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699361152)))];
            tensor<fp16, [1, 77, 1280]> linear_174_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_175_cast)[name = tensor<string, []>("linear_174_cast")];
            tensor<fp16, []> var_2672_to_fp16 = const()[name = tensor<string, []>("op_2672_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_179_cast = mul(x = linear_174_cast, y = var_2672_to_fp16)[name = tensor<string, []>("tensor_179_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(699363776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701002240))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701002816)))];
            tensor<fp16, [1, 77, 1280]> linear_175_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_175_cast)[name = tensor<string, []>("linear_175_cast")];
            tensor<int32, [4]> var_2677 = const()[name = tensor<string, []>("op_2677"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2678_cast = reshape(shape = var_2677, x = linear_175_cast)[name = tensor<string, []>("op_2678_cast")];
            tensor<int32, [4]> var_2679_perm_0 = const()[name = tensor<string, []>("op_2679_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701005440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702643904))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702644480)))];
            tensor<fp16, [1, 77, 1280]> linear_176_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_175_cast)[name = tensor<string, []>("linear_176_cast")];
            tensor<int32, [4]> var_2684 = const()[name = tensor<string, []>("op_2684"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2685_cast = reshape(shape = var_2684, x = linear_176_cast)[name = tensor<string, []>("op_2685_cast")];
            tensor<int32, [4]> var_2686_perm_0 = const()[name = tensor<string, []>("op_2686_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2693 = const()[name = tensor<string, []>("op_2693"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2694_cast = reshape(shape = var_2693, x = tensor_179_cast)[name = tensor<string, []>("op_2694_cast")];
            tensor<int32, [4]> var_2695_perm_0 = const()[name = tensor<string, []>("op_2695_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2697 = const()[name = tensor<string, []>("op_2697"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_14 = transpose(perm = var_2695_perm_0, x = var_2694_cast)[name = tensor<string, []>("transpose_14")];
            tensor<fp16, [20, 77, 64]> query_states_59_cast = reshape(shape = var_2697, x = transpose_14)[name = tensor<string, []>("query_states_59_cast")];
            tensor<int32, [3]> var_2699 = const()[name = tensor<string, []>("op_2699"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_13 = transpose(perm = var_2679_perm_0, x = var_2678_cast)[name = tensor<string, []>("transpose_13")];
            tensor<fp16, [20, 77, 64]> key_states_119_cast = reshape(shape = var_2699, x = transpose_13)[name = tensor<string, []>("key_states_119_cast")];
            tensor<int32, [3]> var_2701 = const()[name = tensor<string, []>("op_2701"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_12 = transpose(perm = var_2686_perm_0, x = var_2685_cast)[name = tensor<string, []>("transpose_12")];
            tensor<fp16, [20, 77, 64]> value_states_119_cast = reshape(shape = var_2701, x = transpose_12)[name = tensor<string, []>("value_states_119_cast")];
            tensor<int32, [3]> var_2704_perm_0 = const()[name = tensor<string, []>("op_2704_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_175_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_175_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_175_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_175_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_11 = transpose(perm = var_2704_perm_0, x = key_states_119_cast)[name = tensor<string, []>("transpose_11")];
            tensor<fp16, [20, 77, 77]> attn_weights_175_cast = matmul(transpose_x = attn_weights_175_transpose_x_0, transpose_y = attn_weights_175_transpose_y_0, x = query_states_59_cast, y = transpose_11)[name = tensor<string, []>("attn_weights_175_cast")];
            tensor<int32, [4]> var_2706 = const()[name = tensor<string, []>("op_2706"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2707_cast = reshape(shape = var_2706, x = attn_weights_175_cast)[name = tensor<string, []>("op_2707_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_177_cast = add(x = var_2707_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_177_cast")];
            tensor<int32, [3]> var_2712 = const()[name = tensor<string, []>("op_2712"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_469_cast = reshape(shape = var_2712, x = attn_weights_177_cast)[name = tensor<string, []>("input_469_cast")];
            tensor<fp16, [20, 77, 77]> input_471_cast = softmax(axis = var_5, x = input_469_cast)[name = tensor<string, []>("input_471_cast")];
            tensor<bool, []> attn_output_175_transpose_x_0 = const()[name = tensor<string, []>("attn_output_175_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_175_transpose_y_0 = const()[name = tensor<string, []>("attn_output_175_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_175_cast = matmul(transpose_x = attn_output_175_transpose_x_0, transpose_y = attn_output_175_transpose_y_0, x = input_471_cast, y = value_states_119_cast)[name = tensor<string, []>("attn_output_175_cast")];
            tensor<int32, [4]> var_2717 = const()[name = tensor<string, []>("op_2717"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_177_cast = reshape(shape = var_2717, x = attn_output_175_cast)[name = tensor<string, []>("attn_output_177_cast")];
            tensor<int32, [4]> attn_output_179_perm_0 = const()[name = tensor<string, []>("attn_output_179_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2720 = const()[name = tensor<string, []>("op_2720"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_10 = transpose(perm = attn_output_179_perm_0, x = attn_output_177_cast)[name = tensor<string, []>("transpose_10")];
            tensor<fp16, [1, 77, 1280]> input_473_cast = reshape(shape = var_2720, x = transpose_10)[name = tensor<string, []>("input_473_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(702647104))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704285568))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704286144)))];
            tensor<fp16, [1, 77, 1280]> linear_177_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16_palettized, x = input_473_cast)[name = tensor<string, []>("linear_177_cast")];
            tensor<fp16, [1, 77, 1280]> input_475_cast = add(x = input_467_cast, y = linear_177_cast)[name = tensor<string, []>("input_475_cast")];
            tensor<int32, [1]> input_477_axes_0 = const()[name = tensor<string, []>("input_477_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704288768)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704291392)))];
            tensor<fp16, [1, 77, 1280]> input_477_cast = layer_norm(axes = input_477_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16, x = input_475_cast)[name = tensor<string, []>("input_477_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704294016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710847680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710848256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710853440))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_178_cast = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16_palettized, x = input_477_cast)[name = tensor<string, []>("linear_178_cast")];
            tensor<string, []> input_481_mode_0 = const()[name = tensor<string, []>("input_481_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_481_cast = gelu(mode = input_481_mode_0, x = linear_178_cast)[name = tensor<string, []>("input_481_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(710854016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717407680))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717408256)))];
            tensor<fp16, [1, 77, 1280]> linear_179_cast = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16_palettized, x = input_481_cast)[name = tensor<string, []>("linear_179_cast")];
            tensor<fp16, [1, 77, 1280]> input_483_cast = add(x = input_475_cast, y = linear_179_cast)[name = tensor<string, []>("input_483_cast")];
            tensor<int32, [1]> hidden_states_181_axes_0 = const()[name = tensor<string, []>("hidden_states_181_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717410880)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717413504)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_181_cast = layer_norm(axes = hidden_states_181_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16, x = input_483_cast)[name = tensor<string, []>("hidden_states_181_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717416128))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719054592))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719055168)))];
            tensor<fp16, [1, 77, 1280]> linear_180_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_181_cast)[name = tensor<string, []>("linear_180_cast")];
            tensor<fp16, []> var_2759_to_fp16 = const()[name = tensor<string, []>("op_2759_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_185_cast = mul(x = linear_180_cast, y = var_2759_to_fp16)[name = tensor<string, []>("tensor_185_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719057792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720696256))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720696832)))];
            tensor<fp16, [1, 77, 1280]> linear_181_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_181_cast)[name = tensor<string, []>("linear_181_cast")];
            tensor<int32, [4]> var_2764 = const()[name = tensor<string, []>("op_2764"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2765_cast = reshape(shape = var_2764, x = linear_181_cast)[name = tensor<string, []>("op_2765_cast")];
            tensor<int32, [4]> var_2766_perm_0 = const()[name = tensor<string, []>("op_2766_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720699456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722337920))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722338496)))];
            tensor<fp16, [1, 77, 1280]> linear_182_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_181_cast)[name = tensor<string, []>("linear_182_cast")];
            tensor<int32, [4]> var_2771 = const()[name = tensor<string, []>("op_2771"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2772_cast = reshape(shape = var_2771, x = linear_182_cast)[name = tensor<string, []>("op_2772_cast")];
            tensor<int32, [4]> var_2773_perm_0 = const()[name = tensor<string, []>("op_2773_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2780 = const()[name = tensor<string, []>("op_2780"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2781_cast = reshape(shape = var_2780, x = tensor_185_cast)[name = tensor<string, []>("op_2781_cast")];
            tensor<int32, [4]> var_2782_perm_0 = const()[name = tensor<string, []>("op_2782_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2784 = const()[name = tensor<string, []>("op_2784"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_9 = transpose(perm = var_2782_perm_0, x = var_2781_cast)[name = tensor<string, []>("transpose_9")];
            tensor<fp16, [20, 77, 64]> query_states_61_cast = reshape(shape = var_2784, x = transpose_9)[name = tensor<string, []>("query_states_61_cast")];
            tensor<int32, [3]> var_2786 = const()[name = tensor<string, []>("op_2786"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_8 = transpose(perm = var_2766_perm_0, x = var_2765_cast)[name = tensor<string, []>("transpose_8")];
            tensor<fp16, [20, 77, 64]> key_states_123_cast = reshape(shape = var_2786, x = transpose_8)[name = tensor<string, []>("key_states_123_cast")];
            tensor<int32, [3]> var_2788 = const()[name = tensor<string, []>("op_2788"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_7 = transpose(perm = var_2773_perm_0, x = var_2772_cast)[name = tensor<string, []>("transpose_7")];
            tensor<fp16, [20, 77, 64]> value_states_123_cast = reshape(shape = var_2788, x = transpose_7)[name = tensor<string, []>("value_states_123_cast")];
            tensor<int32, [3]> var_2791_perm_0 = const()[name = tensor<string, []>("op_2791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_181_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_181_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_181_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_181_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_6 = transpose(perm = var_2791_perm_0, x = key_states_123_cast)[name = tensor<string, []>("transpose_6")];
            tensor<fp16, [20, 77, 77]> attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = query_states_61_cast, y = transpose_6)[name = tensor<string, []>("attn_weights_181_cast")];
            tensor<int32, [4]> var_2793 = const()[name = tensor<string, []>("op_2793"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2794_cast = reshape(shape = var_2793, x = attn_weights_181_cast)[name = tensor<string, []>("op_2794_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_183_cast = add(x = var_2794_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_183_cast")];
            tensor<int32, [3]> var_2799 = const()[name = tensor<string, []>("op_2799"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_485_cast = reshape(shape = var_2799, x = attn_weights_183_cast)[name = tensor<string, []>("input_485_cast")];
            tensor<fp16, [20, 77, 77]> input_487_cast = softmax(axis = var_5, x = input_485_cast)[name = tensor<string, []>("input_487_cast")];
            tensor<bool, []> attn_output_181_transpose_x_0 = const()[name = tensor<string, []>("attn_output_181_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_181_transpose_y_0 = const()[name = tensor<string, []>("attn_output_181_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_181_cast = matmul(transpose_x = attn_output_181_transpose_x_0, transpose_y = attn_output_181_transpose_y_0, x = input_487_cast, y = value_states_123_cast)[name = tensor<string, []>("attn_output_181_cast")];
            tensor<int32, [4]> var_2804 = const()[name = tensor<string, []>("op_2804"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_183_cast = reshape(shape = var_2804, x = attn_output_181_cast)[name = tensor<string, []>("attn_output_183_cast")];
            tensor<int32, [4]> attn_output_185_perm_0 = const()[name = tensor<string, []>("attn_output_185_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2807 = const()[name = tensor<string, []>("op_2807"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_5 = transpose(perm = attn_output_185_perm_0, x = attn_output_183_cast)[name = tensor<string, []>("transpose_5")];
            tensor<fp16, [1, 77, 1280]> input_489_cast = reshape(shape = var_2807, x = transpose_5)[name = tensor<string, []>("input_489_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722341120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723979584))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723980160)))];
            tensor<fp16, [1, 77, 1280]> linear_183_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16_palettized, x = input_489_cast)[name = tensor<string, []>("linear_183_cast")];
            tensor<fp16, [1, 77, 1280]> input_491_cast = add(x = input_483_cast, y = linear_183_cast)[name = tensor<string, []>("input_491_cast")];
            tensor<int32, [1]> input_493_axes_0 = const()[name = tensor<string, []>("input_493_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723982784)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723985408)))];
            tensor<fp16, [1, 77, 1280]> input_493_cast = layer_norm(axes = input_493_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16, x = input_491_cast)[name = tensor<string, []>("input_493_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723988032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730541696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730542272))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730547456))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_184_cast = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16_palettized, x = input_493_cast)[name = tensor<string, []>("linear_184_cast")];
            tensor<string, []> input_497_mode_0 = const()[name = tensor<string, []>("input_497_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_497_cast = gelu(mode = input_497_mode_0, x = linear_184_cast)[name = tensor<string, []>("input_497_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730548032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737101696))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737102272)))];
            tensor<fp16, [1, 77, 1280]> linear_185_cast = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16_palettized, x = input_497_cast)[name = tensor<string, []>("linear_185_cast")];
            tensor<fp16, [1, 77, 1280]> input_499_cast = add(x = input_491_cast, y = linear_185_cast)[name = tensor<string, []>("input_499_cast")];
            tensor<string, []> input_499_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("input_499_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [1]> hidden_states_187_axes_0 = const()[name = tensor<string, []>("hidden_states_187_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737104896)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737107520)))];
            tensor<fp16, [1, 77, 1280]> hidden_states_187_cast = layer_norm(axes = hidden_states_187_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16, x = input_499_cast)[name = tensor<string, []>("hidden_states_187_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737110144))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738748608))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738749184)))];
            tensor<fp16, [1, 77, 1280]> linear_186_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("linear_186_cast")];
            tensor<fp16, []> var_2846_to_fp16 = const()[name = tensor<string, []>("op_2846_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1280]> tensor_cast = mul(x = linear_186_cast, y = var_2846_to_fp16)[name = tensor<string, []>("tensor_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(738751808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740390272))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740390848)))];
            tensor<fp16, [1, 77, 1280]> linear_187_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("linear_187_cast")];
            tensor<int32, [4]> var_2851 = const()[name = tensor<string, []>("op_2851"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2852_cast = reshape(shape = var_2851, x = linear_187_cast)[name = tensor<string, []>("op_2852_cast")];
            tensor<int32, [4]> var_2853_perm_0 = const()[name = tensor<string, []>("op_2853_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(740393472))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742031936))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742032512)))];
            tensor<fp16, [1, 77, 1280]> linear_188_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("linear_188_cast")];
            tensor<int32, [4]> var_2858 = const()[name = tensor<string, []>("op_2858"), val = tensor<int32, [4]>([1, -1, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2859_cast = reshape(shape = var_2858, x = linear_188_cast)[name = tensor<string, []>("op_2859_cast")];
            tensor<int32, [4]> var_2860_perm_0 = const()[name = tensor<string, []>("op_2860_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2867 = const()[name = tensor<string, []>("op_2867"), val = tensor<int32, [4]>([1, 77, 20, 64])];
            tensor<fp16, [1, 77, 20, 64]> var_2868_cast = reshape(shape = var_2867, x = tensor_cast)[name = tensor<string, []>("op_2868_cast")];
            tensor<int32, [4]> var_2869_perm_0 = const()[name = tensor<string, []>("op_2869_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2871 = const()[name = tensor<string, []>("op_2871"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_4 = transpose(perm = var_2869_perm_0, x = var_2868_cast)[name = tensor<string, []>("transpose_4")];
            tensor<fp16, [20, 77, 64]> query_states_cast = reshape(shape = var_2871, x = transpose_4)[name = tensor<string, []>("query_states_cast")];
            tensor<int32, [3]> var_2873 = const()[name = tensor<string, []>("op_2873"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_3 = transpose(perm = var_2853_perm_0, x = var_2852_cast)[name = tensor<string, []>("transpose_3")];
            tensor<fp16, [20, 77, 64]> key_states_cast = reshape(shape = var_2873, x = transpose_3)[name = tensor<string, []>("key_states_cast")];
            tensor<int32, [3]> var_2875 = const()[name = tensor<string, []>("op_2875"), val = tensor<int32, [3]>([20, -1, 64])];
            tensor<fp16, [1, 20, 77, 64]> transpose_2 = transpose(perm = var_2860_perm_0, x = var_2859_cast)[name = tensor<string, []>("transpose_2")];
            tensor<fp16, [20, 77, 64]> value_states_cast = reshape(shape = var_2875, x = transpose_2)[name = tensor<string, []>("value_states_cast")];
            tensor<int32, [3]> var_2878_perm_0 = const()[name = tensor<string, []>("op_2878_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_187_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_187_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_187_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_187_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 64, 77]> transpose_1 = transpose(perm = var_2878_perm_0, x = key_states_cast)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [20, 77, 77]> attn_weights_187_cast = matmul(transpose_x = attn_weights_187_transpose_x_0, transpose_y = attn_weights_187_transpose_y_0, x = query_states_cast, y = transpose_1)[name = tensor<string, []>("attn_weights_187_cast")];
            tensor<int32, [4]> var_2880 = const()[name = tensor<string, []>("op_2880"), val = tensor<int32, [4]>([1, 20, 77, 77])];
            tensor<fp16, [1, 20, 77, 77]> var_2881_cast = reshape(shape = var_2880, x = attn_weights_187_cast)[name = tensor<string, []>("op_2881_cast")];
            tensor<fp16, [1, 20, 77, 77]> attn_weights_189_cast = add(x = var_2881_cast, y = op_58_to_fp16_palettized)[name = tensor<string, []>("attn_weights_189_cast")];
            tensor<int32, [3]> var_2886 = const()[name = tensor<string, []>("op_2886"), val = tensor<int32, [3]>([20, 77, 77])];
            tensor<fp16, [20, 77, 77]> input_501_cast = reshape(shape = var_2886, x = attn_weights_189_cast)[name = tensor<string, []>("input_501_cast")];
            tensor<fp16, [20, 77, 77]> input_503_cast = softmax(axis = var_5, x = input_501_cast)[name = tensor<string, []>("input_503_cast")];
            tensor<bool, []> attn_output_187_transpose_x_0 = const()[name = tensor<string, []>("attn_output_187_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_187_transpose_y_0 = const()[name = tensor<string, []>("attn_output_187_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [20, 77, 64]> attn_output_187_cast = matmul(transpose_x = attn_output_187_transpose_x_0, transpose_y = attn_output_187_transpose_y_0, x = input_503_cast, y = value_states_cast)[name = tensor<string, []>("attn_output_187_cast")];
            tensor<int32, [4]> var_2891 = const()[name = tensor<string, []>("op_2891"), val = tensor<int32, [4]>([1, 20, 77, 64])];
            tensor<fp16, [1, 20, 77, 64]> attn_output_189_cast = reshape(shape = var_2891, x = attn_output_187_cast)[name = tensor<string, []>("attn_output_189_cast")];
            tensor<int32, [4]> attn_output_perm_0 = const()[name = tensor<string, []>("attn_output_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2894 = const()[name = tensor<string, []>("op_2894"), val = tensor<int32, [3]>([1, 77, 1280])];
            tensor<fp16, [1, 77, 20, 64]> transpose_0 = transpose(perm = attn_output_perm_0, x = attn_output_189_cast)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [1, 77, 1280]> input_505_cast = reshape(shape = var_2894, x = transpose_0)[name = tensor<string, []>("input_505_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742035136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743673600))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743674176)))];
            tensor<fp16, [1, 77, 1280]> linear_189_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16_palettized, x = input_505_cast)[name = tensor<string, []>("linear_189_cast")];
            tensor<fp16, [1, 77, 1280]> input_507_cast = add(x = input_499_cast, y = linear_189_cast)[name = tensor<string, []>("input_507_cast")];
            tensor<int32, [1]> input_509_axes_0 = const()[name = tensor<string, []>("input_509_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743676800)))];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743679424)))];
            tensor<fp16, [1, 77, 1280]> input_509_cast = layer_norm(axes = input_509_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16, x = input_507_cast)[name = tensor<string, []>("input_509_cast")];
            tensor<fp16, [5120, 1280]> text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(743682048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750235712))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([5120, 1280])];
            tensor<fp16, [5120]> text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750236288))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750241472))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [1, 77, 5120]> linear_190_cast = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16_palettized, x = input_509_cast)[name = tensor<string, []>("linear_190_cast")];
            tensor<string, []> input_513_mode_0 = const()[name = tensor<string, []>("input_513_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 5120]> input_513_cast = gelu(mode = input_513_mode_0, x = linear_190_cast)[name = tensor<string, []>("input_513_cast")];
            tensor<fp16, [1280, 5120]> text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750242048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756795712))), name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 5120])];
            tensor<fp16, [1280]> text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756796288)))];
            tensor<fp16, [1, 77, 1280]> linear_191_cast = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16_palettized, x = input_513_cast)[name = tensor<string, []>("linear_191_cast")];
            tensor<fp16, [1, 77, 1280]> input_515_cast = add(x = input_507_cast, y = linear_191_cast)[name = tensor<string, []>("input_515_cast")];
            tensor<int32, [1]> last_hidden_state_axes_0 = const()[name = tensor<string, []>("last_hidden_state_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1280]> text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756798912)))];
            tensor<fp16, [1280]> text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756801536)))];
            tensor<fp16, [1, 77, 1280]> last_hidden_state_cast = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_515_cast)[name = tensor<string, []>("last_hidden_state_cast")];
            tensor<int32, [1]> var_2922 = const()[name = tensor<string, []>("op_2922"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> var_2924 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_2)[name = tensor<string, []>("op_2924")];
            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(1)];
            tensor<int32, [1, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (var_2922, var_2924))[name = tensor<string, []>("stack_0")];
            tensor<int32, []> input_transpose_batch_dims_0 = const()[name = tensor<string, []>("input_transpose_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [1, 1280]> input_transpose_cast = gather_nd(batch_dims = input_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast)[name = tensor<string, []>("input_transpose_cast")];
            tensor<fp16, [1280, 1280]> text_encoder_text_projection_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756804160))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758442624))), name = tensor<string, []>("text_encoder_text_projection_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1280, 1280])];
            tensor<fp16, [1280]> linear_192_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_192_bias_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758443200)))];
            tensor<fp16, [1, 1280]> linear_192_cast = linear(bias = linear_192_bias_0_to_fp16, weight = text_encoder_text_projection_weight_to_fp16_palettized, x = input_transpose_cast)[name = tensor<string, []>("linear_192_cast")];
            tensor<string, []> linear_192_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_192_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 77, 1280]> hidden_embeds = cast(dtype = input_499_cast_to_fp32_dtype_0, x = input_499_cast)[name = tensor<string, []>("cast_0")];
            tensor<fp32, [1, 1280]> pooled_outputs = cast(dtype = linear_192_cast_to_fp32_dtype_0, x = linear_192_cast)[name = tensor<string, []>("cast_1")];
        } -> (hidden_embeds, pooled_outputs);
}