program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] { func main(tensor z) { tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("valid")]; tensor input_1_strides_0 = const()[name = tensor("input_1_strides_0"), val = tensor([1, 1])]; tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_1_dilations_0 = const()[name = tensor("input_1_dilations_0"), val = tensor([1, 1])]; tensor input_1_groups_0 = const()[name = tensor("input_1_groups_0"), val = tensor(1)]; tensor post_quant_conv_weight_to_fp16 = const()[name = tensor("post_quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor post_quant_conv_bias_to_fp16 = const()[name = tensor("post_quant_conv_bias_to_fp16"), val = tensor([-0x1.a6p-6, -0x1.9f4p-4, -0x1.b58p-3, 0x1.7fp-3])]; tensor input_1_cast_fp16 = conv(bias = post_quant_conv_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = post_quant_conv_weight_to_fp16, x = z)[name = tensor("input_1_cast_fp16")]; tensor var_24 = const()[name = tensor("op_24"), val = tensor(-1)]; tensor var_46_begin_0 = const()[name = tensor("op_46_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_46_end_0 = const()[name = tensor("op_46_end_0"), val = tensor([1, 4, 64, 128])]; tensor var_46_end_mask_0 = const()[name = tensor("op_46_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = input_1_cast_fp16)[name = tensor("op_46_cast_fp16")]; tensor var_47_begin_0 = const()[name = tensor("op_47_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_47_end_0 = const()[name = tensor("op_47_end_0"), val = tensor([1, 4, 64, 1])]; tensor var_47_end_mask_0 = const()[name = tensor("op_47_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, x = input_1_cast_fp16)[name = tensor("op_47_cast_fp16")]; tensor input_3_interleave_0 = const()[name = tensor("input_3_interleave_0"), val = tensor(false)]; tensor input_3_cast_fp16 = concat(axis = var_24, interleave = input_3_interleave_0, values = (var_46_cast_fp16, input_1_cast_fp16, var_47_cast_fp16))[name = tensor("input_3_cast_fp16")]; tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_5_mode_0 = const()[name = tensor("input_5_mode_0"), val = tensor("constant")]; tensor const_0_to_fp16 = const()[name = tensor("const_0_to_fp16"), val = tensor(0x0p+0)]; tensor input_5_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("valid")]; tensor input_7_strides_0 = const()[name = tensor("input_7_strides_0"), val = tensor([1, 1])]; tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_7_dilations_0 = const()[name = tensor("input_7_dilations_0"), val = tensor([1, 1])]; tensor input_7_groups_0 = const()[name = tensor("input_7_groups_0"), val = tensor(1)]; tensor decoder_conv_in_weight_to_fp16 = const()[name = tensor("decoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; tensor decoder_conv_in_bias_to_fp16 = const()[name = tensor("decoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37120)))]; tensor input_7_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = decoder_conv_in_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38208)))]; tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39296)))]; tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40384)))]; tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41472)))]; tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; tensor input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_11_cast_fp16")]; tensor var_73_begin_0 = const()[name = tensor("op_73_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_73_end_0 = const()[name = tensor("op_73_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_73_end_mask_0 = const()[name = tensor("op_73_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_73_cast_fp16 = slice_by_index(begin = var_73_begin_0, end = var_73_end_0, end_mask = var_73_end_mask_0, x = input_11_cast_fp16)[name = tensor("op_73_cast_fp16")]; tensor var_74_begin_0 = const()[name = tensor("op_74_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_74_end_0 = const()[name = tensor("op_74_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_74_end_mask_0 = const()[name = tensor("op_74_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_74_cast_fp16 = slice_by_index(begin = var_74_begin_0, end = var_74_end_0, end_mask = var_74_end_mask_0, x = input_11_cast_fp16)[name = tensor("op_74_cast_fp16")]; tensor input_13_interleave_0 = const()[name = tensor("input_13_interleave_0"), val = tensor(false)]; tensor input_13_cast_fp16 = concat(axis = var_24, interleave = input_13_interleave_0, values = (var_73_cast_fp16, input_11_cast_fp16, var_74_cast_fp16))[name = tensor("input_13_cast_fp16")]; tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_15_mode_0 = const()[name = tensor("input_15_mode_0"), val = tensor("constant")]; tensor const_1_to_fp16 = const()[name = tensor("const_1_to_fp16"), val = tensor(0x0p+0)]; tensor input_15_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_15_mode_0, pad = input_15_pad_0, x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1, 1])]; tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1, 1])]; tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; tensor decoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42560)))]; tensor decoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4761216)))]; tensor input_17_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("input_17_cast_fp16")]; tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4762304)))]; tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4763392)))]; tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; tensor input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_21_cast_fp16")]; tensor var_91_begin_0 = const()[name = tensor("op_91_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_91_end_0 = const()[name = tensor("op_91_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_91_end_mask_0 = const()[name = tensor("op_91_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_91_cast_fp16 = slice_by_index(begin = var_91_begin_0, end = var_91_end_0, end_mask = var_91_end_mask_0, x = input_21_cast_fp16)[name = tensor("op_91_cast_fp16")]; tensor var_92_begin_0 = const()[name = tensor("op_92_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_92_end_0 = const()[name = tensor("op_92_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_92_end_mask_0 = const()[name = tensor("op_92_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_92_cast_fp16 = slice_by_index(begin = var_92_begin_0, end = var_92_end_0, end_mask = var_92_end_mask_0, x = input_21_cast_fp16)[name = tensor("op_92_cast_fp16")]; tensor input_25_interleave_0 = const()[name = tensor("input_25_interleave_0"), val = tensor(false)]; tensor input_25_cast_fp16 = concat(axis = var_24, interleave = input_25_interleave_0, values = (var_91_cast_fp16, input_21_cast_fp16, var_92_cast_fp16))[name = tensor("input_25_cast_fp16")]; tensor input_27_pad_0 = const()[name = tensor("input_27_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_27_mode_0 = const()[name = tensor("input_27_mode_0"), val = tensor("constant")]; tensor const_2_to_fp16 = const()[name = tensor("const_2_to_fp16"), val = tensor(0x0p+0)]; tensor input_27_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_27_mode_0, pad = input_27_pad_0, x = input_25_cast_fp16)[name = tensor("input_27_cast_fp16")]; tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("valid")]; tensor hidden_states_1_strides_0 = const()[name = tensor("hidden_states_1_strides_0"), val = tensor([1, 1])]; tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_1_dilations_0 = const()[name = tensor("hidden_states_1_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_1_groups_0 = const()[name = tensor("hidden_states_1_groups_0"), val = tensor(1)]; tensor decoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4764480)))]; tensor decoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9483136)))]; tensor hidden_states_1_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_1_dilations_0, groups = hidden_states_1_groups_0, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = hidden_states_1_strides_0, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; tensor var_102_cast_fp16 = add(x = input_7_cast_fp16, y = hidden_states_1_cast_fp16)[name = tensor("op_102_cast_fp16")]; tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 8192])]; tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_102_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 8192])]; tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; tensor reshape_10_to_fp16 = const()[name = tensor("reshape_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9484224)))]; tensor mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor("mul_2_cast_fp16")]; tensor reshape_11_to_fp16 = const()[name = tensor("reshape_11_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9485312)))]; tensor add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor("add_5_cast_fp16")]; tensor input_31_perm_0 = const()[name = tensor("input_31_perm_0"), val = tensor([0, 2, 1])]; tensor decoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486400)))]; tensor decoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10010752)))]; tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = add_5_cast_fp16)[name = tensor("transpose_11")]; tensor linear_0_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_q_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("linear_0_cast_fp16")]; tensor decoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10011840)))]; tensor decoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10536192)))]; tensor linear_1_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_k_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("linear_1_cast_fp16")]; tensor decoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10537280)))]; tensor decoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11061632)))]; tensor linear_2_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_v_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("linear_2_cast_fp16")]; tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, -1, 1, 512])]; tensor var_144_cast_fp16 = reshape(shape = var_143, x = linear_0_cast_fp16)[name = tensor("op_144_cast_fp16")]; tensor var_146 = const()[name = tensor("op_146"), val = tensor([1, -1, 1, 512])]; tensor var_147_cast_fp16 = reshape(shape = var_146, x = linear_1_cast_fp16)[name = tensor("op_147_cast_fp16")]; tensor var_149 = const()[name = tensor("op_149"), val = tensor([1, -1, 1, 512])]; tensor var_150_cast_fp16 = reshape(shape = var_149, x = linear_2_cast_fp16)[name = tensor("op_150_cast_fp16")]; tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; tensor mul_3_y_0_to_fp16 = const()[name = tensor("mul_3_y_0_to_fp16"), val = tensor(0x1.6ap-5)]; tensor mul_3_cast_fp16 = mul(x = var_144_cast_fp16, y = mul_3_y_0_to_fp16)[name = tensor("mul_3_cast_fp16")]; tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_5 = transpose(perm = transpose_5_perm_0, x = var_147_cast_fp16)[name = tensor("transpose_8")]; tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_3_cast_fp16)[name = tensor("transpose_9")]; tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_4, y = transpose_5)[name = tensor("matmul_0_cast_fp16")]; tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; tensor softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = matmul_0_cast_fp16)[name = tensor("softmax_0_cast_fp16")]; tensor hidden_states_7_transpose_x_0 = const()[name = tensor("hidden_states_7_transpose_x_0"), val = tensor(false)]; tensor hidden_states_7_transpose_y_0 = const()[name = tensor("hidden_states_7_transpose_y_0"), val = tensor(false)]; tensor value_cast_fp16 = transpose(perm = value_perm_0, x = var_150_cast_fp16)[name = tensor("transpose_10")]; tensor hidden_states_7_cast_fp16 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0_cast_fp16, y = value_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; tensor var_153_perm_0 = const()[name = tensor("op_153_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_157 = const()[name = tensor("op_157"), val = tensor([1, -1, 512])]; tensor var_153_cast_fp16 = transpose(perm = var_153_perm_0, x = hidden_states_7_cast_fp16)[name = tensor("transpose_7")]; tensor hidden_states_9_cast_fp16 = reshape(shape = var_157, x = var_153_cast_fp16)[name = tensor("hidden_states_9_cast_fp16")]; tensor decoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11062720)))]; tensor decoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11587072)))]; tensor linear_3_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("linear_3_cast_fp16")]; tensor var_164_perm_0 = const()[name = tensor("op_164_perm_0"), val = tensor([0, -1, -2])]; tensor var_165 = const()[name = tensor("op_165"), val = tensor([1, 512, 64, 128])]; tensor var_164_cast_fp16 = transpose(perm = var_164_perm_0, x = linear_3_cast_fp16)[name = tensor("transpose_6")]; tensor hidden_states_13_cast_fp16 = reshape(shape = var_165, x = var_164_cast_fp16)[name = tensor("hidden_states_13_cast_fp16")]; tensor hidden_states_15_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_102_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = hidden_states_15_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11588160)))]; tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11589248)))]; tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; tensor input_41_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_41_cast_fp16")]; tensor var_180_begin_0 = const()[name = tensor("op_180_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_180_end_0 = const()[name = tensor("op_180_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_180_end_mask_0 = const()[name = tensor("op_180_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_180_cast_fp16 = slice_by_index(begin = var_180_begin_0, end = var_180_end_0, end_mask = var_180_end_mask_0, x = input_41_cast_fp16)[name = tensor("op_180_cast_fp16")]; tensor var_181_begin_0 = const()[name = tensor("op_181_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_181_end_0 = const()[name = tensor("op_181_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_181_end_mask_0 = const()[name = tensor("op_181_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_181_cast_fp16 = slice_by_index(begin = var_181_begin_0, end = var_181_end_0, end_mask = var_181_end_mask_0, x = input_41_cast_fp16)[name = tensor("op_181_cast_fp16")]; tensor input_43_interleave_0 = const()[name = tensor("input_43_interleave_0"), val = tensor(false)]; tensor input_43_cast_fp16 = concat(axis = var_24, interleave = input_43_interleave_0, values = (var_180_cast_fp16, input_41_cast_fp16, var_181_cast_fp16))[name = tensor("input_43_cast_fp16")]; tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_45_mode_0 = const()[name = tensor("input_45_mode_0"), val = tensor("constant")]; tensor const_9_to_fp16 = const()[name = tensor("const_9_to_fp16"), val = tensor(0x0p+0)]; tensor input_45_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_45_mode_0, pad = input_45_pad_0, x = input_43_cast_fp16)[name = tensor("input_45_cast_fp16")]; tensor input_47_pad_type_0 = const()[name = tensor("input_47_pad_type_0"), val = tensor("valid")]; tensor input_47_strides_0 = const()[name = tensor("input_47_strides_0"), val = tensor([1, 1])]; tensor input_47_pad_0 = const()[name = tensor("input_47_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_47_dilations_0 = const()[name = tensor("input_47_dilations_0"), val = tensor([1, 1])]; tensor input_47_groups_0 = const()[name = tensor("input_47_groups_0"), val = tensor(1)]; tensor decoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11590336)))]; tensor decoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16308992)))]; tensor input_47_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_47_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16310080)))]; tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16311168)))]; tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; tensor input_51_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_51_cast_fp16")]; tensor var_198_begin_0 = const()[name = tensor("op_198_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_198_end_0 = const()[name = tensor("op_198_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_198_end_mask_0 = const()[name = tensor("op_198_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_198_cast_fp16 = slice_by_index(begin = var_198_begin_0, end = var_198_end_0, end_mask = var_198_end_mask_0, x = input_51_cast_fp16)[name = tensor("op_198_cast_fp16")]; tensor var_199_begin_0 = const()[name = tensor("op_199_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_199_end_0 = const()[name = tensor("op_199_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_199_end_mask_0 = const()[name = tensor("op_199_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_199_cast_fp16 = slice_by_index(begin = var_199_begin_0, end = var_199_end_0, end_mask = var_199_end_mask_0, x = input_51_cast_fp16)[name = tensor("op_199_cast_fp16")]; tensor input_55_interleave_0 = const()[name = tensor("input_55_interleave_0"), val = tensor(false)]; tensor input_55_cast_fp16 = concat(axis = var_24, interleave = input_55_interleave_0, values = (var_198_cast_fp16, input_51_cast_fp16, var_199_cast_fp16))[name = tensor("input_55_cast_fp16")]; tensor input_57_pad_0 = const()[name = tensor("input_57_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_57_mode_0 = const()[name = tensor("input_57_mode_0"), val = tensor("constant")]; tensor const_10_to_fp16 = const()[name = tensor("const_10_to_fp16"), val = tensor(0x0p+0)]; tensor input_57_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_57_mode_0, pad = input_57_pad_0, x = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("valid")]; tensor hidden_states_17_strides_0 = const()[name = tensor("hidden_states_17_strides_0"), val = tensor([1, 1])]; tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_17_dilations_0 = const()[name = tensor("hidden_states_17_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_17_groups_0 = const()[name = tensor("hidden_states_17_groups_0"), val = tensor(1)]; tensor decoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16312256)))]; tensor decoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21030912)))]; tensor hidden_states_17_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; tensor var_209_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = tensor("op_209_cast_fp16")]; tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_209_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21032000)))]; tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21033088)))]; tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; tensor input_63_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("input_63_cast_fp16")]; tensor var_231_begin_0 = const()[name = tensor("op_231_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_231_end_0 = const()[name = tensor("op_231_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_231_end_mask_0 = const()[name = tensor("op_231_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_231_cast_fp16 = slice_by_index(begin = var_231_begin_0, end = var_231_end_0, end_mask = var_231_end_mask_0, x = input_63_cast_fp16)[name = tensor("op_231_cast_fp16")]; tensor var_232_begin_0 = const()[name = tensor("op_232_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_232_end_0 = const()[name = tensor("op_232_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_232_end_mask_0 = const()[name = tensor("op_232_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_232_cast_fp16 = slice_by_index(begin = var_232_begin_0, end = var_232_end_0, end_mask = var_232_end_mask_0, x = input_63_cast_fp16)[name = tensor("op_232_cast_fp16")]; tensor input_65_interleave_0 = const()[name = tensor("input_65_interleave_0"), val = tensor(false)]; tensor input_65_cast_fp16 = concat(axis = var_24, interleave = input_65_interleave_0, values = (var_231_cast_fp16, input_63_cast_fp16, var_232_cast_fp16))[name = tensor("input_65_cast_fp16")]; tensor input_67_pad_0 = const()[name = tensor("input_67_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_67_mode_0 = const()[name = tensor("input_67_mode_0"), val = tensor("constant")]; tensor const_11_to_fp16 = const()[name = tensor("const_11_to_fp16"), val = tensor(0x0p+0)]; tensor input_67_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = tensor("input_67_cast_fp16")]; tensor input_69_pad_type_0 = const()[name = tensor("input_69_pad_type_0"), val = tensor("valid")]; tensor input_69_strides_0 = const()[name = tensor("input_69_strides_0"), val = tensor([1, 1])]; tensor input_69_pad_0 = const()[name = tensor("input_69_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_69_dilations_0 = const()[name = tensor("input_69_dilations_0"), val = tensor([1, 1])]; tensor input_69_groups_0 = const()[name = tensor("input_69_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21034176)))]; tensor decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25752832)))]; tensor input_69_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("input_69_cast_fp16")]; tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_69_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25753920)))]; tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25755008)))]; tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; tensor input_73_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_73_cast_fp16")]; tensor var_249_begin_0 = const()[name = tensor("op_249_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_249_end_0 = const()[name = tensor("op_249_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_249_end_mask_0 = const()[name = tensor("op_249_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_249_cast_fp16 = slice_by_index(begin = var_249_begin_0, end = var_249_end_0, end_mask = var_249_end_mask_0, x = input_73_cast_fp16)[name = tensor("op_249_cast_fp16")]; tensor var_250_begin_0 = const()[name = tensor("op_250_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_250_end_0 = const()[name = tensor("op_250_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_250_end_mask_0 = const()[name = tensor("op_250_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_250_cast_fp16 = slice_by_index(begin = var_250_begin_0, end = var_250_end_0, end_mask = var_250_end_mask_0, x = input_73_cast_fp16)[name = tensor("op_250_cast_fp16")]; tensor input_77_interleave_0 = const()[name = tensor("input_77_interleave_0"), val = tensor(false)]; tensor input_77_cast_fp16 = concat(axis = var_24, interleave = input_77_interleave_0, values = (var_249_cast_fp16, input_73_cast_fp16, var_250_cast_fp16))[name = tensor("input_77_cast_fp16")]; tensor input_79_pad_0 = const()[name = tensor("input_79_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_79_mode_0 = const()[name = tensor("input_79_mode_0"), val = tensor("constant")]; tensor const_12_to_fp16 = const()[name = tensor("const_12_to_fp16"), val = tensor(0x0p+0)]; tensor input_79_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_79_mode_0, pad = input_79_pad_0, x = input_77_cast_fp16)[name = tensor("input_79_cast_fp16")]; tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("valid")]; tensor hidden_states_19_strides_0 = const()[name = tensor("hidden_states_19_strides_0"), val = tensor([1, 1])]; tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_19_dilations_0 = const()[name = tensor("hidden_states_19_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_19_groups_0 = const()[name = tensor("hidden_states_19_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25756096)))]; tensor decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30474752)))]; tensor hidden_states_19_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; tensor var_260_cast_fp16 = add(x = var_209_cast_fp16, y = hidden_states_19_cast_fp16)[name = tensor("op_260_cast_fp16")]; tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_260_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30475840)))]; tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30476928)))]; tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; tensor input_85_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_85_cast_fp16")]; tensor var_273_begin_0 = const()[name = tensor("op_273_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_273_end_0 = const()[name = tensor("op_273_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_273_end_mask_0 = const()[name = tensor("op_273_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_273_cast_fp16 = slice_by_index(begin = var_273_begin_0, end = var_273_end_0, end_mask = var_273_end_mask_0, x = input_85_cast_fp16)[name = tensor("op_273_cast_fp16")]; tensor var_274_begin_0 = const()[name = tensor("op_274_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_274_end_0 = const()[name = tensor("op_274_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_274_end_mask_0 = const()[name = tensor("op_274_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_274_cast_fp16 = slice_by_index(begin = var_274_begin_0, end = var_274_end_0, end_mask = var_274_end_mask_0, x = input_85_cast_fp16)[name = tensor("op_274_cast_fp16")]; tensor input_87_interleave_0 = const()[name = tensor("input_87_interleave_0"), val = tensor(false)]; tensor input_87_cast_fp16 = concat(axis = var_24, interleave = input_87_interleave_0, values = (var_273_cast_fp16, input_85_cast_fp16, var_274_cast_fp16))[name = tensor("input_87_cast_fp16")]; tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_89_mode_0 = const()[name = tensor("input_89_mode_0"), val = tensor("constant")]; tensor const_13_to_fp16 = const()[name = tensor("const_13_to_fp16"), val = tensor(0x0p+0)]; tensor input_89_cast_fp16 = pad(constant_val = const_13_to_fp16, mode = input_89_mode_0, pad = input_89_pad_0, x = input_87_cast_fp16)[name = tensor("input_89_cast_fp16")]; tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("valid")]; tensor input_91_strides_0 = const()[name = tensor("input_91_strides_0"), val = tensor([1, 1])]; tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_91_dilations_0 = const()[name = tensor("input_91_dilations_0"), val = tensor([1, 1])]; tensor input_91_groups_0 = const()[name = tensor("input_91_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30478016)))]; tensor decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35196672)))]; tensor input_91_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("input_91_cast_fp16")]; tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_91_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35197760)))]; tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35198848)))]; tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; tensor input_95_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("input_95_cast_fp16")]; tensor var_291_begin_0 = const()[name = tensor("op_291_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_291_end_0 = const()[name = tensor("op_291_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_291_end_mask_0 = const()[name = tensor("op_291_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_291_cast_fp16 = slice_by_index(begin = var_291_begin_0, end = var_291_end_0, end_mask = var_291_end_mask_0, x = input_95_cast_fp16)[name = tensor("op_291_cast_fp16")]; tensor var_292_begin_0 = const()[name = tensor("op_292_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_292_end_0 = const()[name = tensor("op_292_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_292_end_mask_0 = const()[name = tensor("op_292_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_292_cast_fp16 = slice_by_index(begin = var_292_begin_0, end = var_292_end_0, end_mask = var_292_end_mask_0, x = input_95_cast_fp16)[name = tensor("op_292_cast_fp16")]; tensor input_99_interleave_0 = const()[name = tensor("input_99_interleave_0"), val = tensor(false)]; tensor input_99_cast_fp16 = concat(axis = var_24, interleave = input_99_interleave_0, values = (var_291_cast_fp16, input_95_cast_fp16, var_292_cast_fp16))[name = tensor("input_99_cast_fp16")]; tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_101_mode_0 = const()[name = tensor("input_101_mode_0"), val = tensor("constant")]; tensor const_14_to_fp16 = const()[name = tensor("const_14_to_fp16"), val = tensor(0x0p+0)]; tensor input_101_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_101_mode_0, pad = input_101_pad_0, x = input_99_cast_fp16)[name = tensor("input_101_cast_fp16")]; tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("valid")]; tensor hidden_states_21_strides_0 = const()[name = tensor("hidden_states_21_strides_0"), val = tensor([1, 1])]; tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_21_dilations_0 = const()[name = tensor("hidden_states_21_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_21_groups_0 = const()[name = tensor("hidden_states_21_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35199936)))]; tensor decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39918592)))]; tensor hidden_states_21_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_21_dilations_0, groups = hidden_states_21_groups_0, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = hidden_states_21_strides_0, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_101_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; tensor var_302_cast_fp16 = add(x = var_260_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor("op_302_cast_fp16")]; tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_302_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39919680)))]; tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39920768)))]; tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; tensor input_107_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("input_107_cast_fp16")]; tensor var_315_begin_0 = const()[name = tensor("op_315_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_315_end_0 = const()[name = tensor("op_315_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_315_end_mask_0 = const()[name = tensor("op_315_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_315_cast_fp16 = slice_by_index(begin = var_315_begin_0, end = var_315_end_0, end_mask = var_315_end_mask_0, x = input_107_cast_fp16)[name = tensor("op_315_cast_fp16")]; tensor var_316_begin_0 = const()[name = tensor("op_316_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_316_end_0 = const()[name = tensor("op_316_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_316_end_mask_0 = const()[name = tensor("op_316_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_316_cast_fp16 = slice_by_index(begin = var_316_begin_0, end = var_316_end_0, end_mask = var_316_end_mask_0, x = input_107_cast_fp16)[name = tensor("op_316_cast_fp16")]; tensor input_109_interleave_0 = const()[name = tensor("input_109_interleave_0"), val = tensor(false)]; tensor input_109_cast_fp16 = concat(axis = var_24, interleave = input_109_interleave_0, values = (var_315_cast_fp16, input_107_cast_fp16, var_316_cast_fp16))[name = tensor("input_109_cast_fp16")]; tensor input_111_pad_0 = const()[name = tensor("input_111_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_111_mode_0 = const()[name = tensor("input_111_mode_0"), val = tensor("constant")]; tensor const_15_to_fp16 = const()[name = tensor("const_15_to_fp16"), val = tensor(0x0p+0)]; tensor input_111_cast_fp16 = pad(constant_val = const_15_to_fp16, mode = input_111_mode_0, pad = input_111_pad_0, x = input_109_cast_fp16)[name = tensor("input_111_cast_fp16")]; tensor input_113_pad_type_0 = const()[name = tensor("input_113_pad_type_0"), val = tensor("valid")]; tensor input_113_strides_0 = const()[name = tensor("input_113_strides_0"), val = tensor([1, 1])]; tensor input_113_pad_0 = const()[name = tensor("input_113_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_113_dilations_0 = const()[name = tensor("input_113_dilations_0"), val = tensor([1, 1])]; tensor input_113_groups_0 = const()[name = tensor("input_113_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39921856)))]; tensor decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44640512)))]; tensor input_113_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("input_113_cast_fp16")]; tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 64, 128])]; tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_113_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 64, 128])]; tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44641600)))]; tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44642688)))]; tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; tensor input_117_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_117_cast_fp16")]; tensor var_333_begin_0 = const()[name = tensor("op_333_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_333_end_0 = const()[name = tensor("op_333_end_0"), val = tensor([1, 512, 64, 128])]; tensor var_333_end_mask_0 = const()[name = tensor("op_333_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_333_cast_fp16 = slice_by_index(begin = var_333_begin_0, end = var_333_end_0, end_mask = var_333_end_mask_0, x = input_117_cast_fp16)[name = tensor("op_333_cast_fp16")]; tensor var_334_begin_0 = const()[name = tensor("op_334_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_334_end_0 = const()[name = tensor("op_334_end_0"), val = tensor([1, 512, 64, 1])]; tensor var_334_end_mask_0 = const()[name = tensor("op_334_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_334_cast_fp16 = slice_by_index(begin = var_334_begin_0, end = var_334_end_0, end_mask = var_334_end_mask_0, x = input_117_cast_fp16)[name = tensor("op_334_cast_fp16")]; tensor input_121_interleave_0 = const()[name = tensor("input_121_interleave_0"), val = tensor(false)]; tensor input_121_cast_fp16 = concat(axis = var_24, interleave = input_121_interleave_0, values = (var_333_cast_fp16, input_117_cast_fp16, var_334_cast_fp16))[name = tensor("input_121_cast_fp16")]; tensor input_123_pad_0 = const()[name = tensor("input_123_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_123_mode_0 = const()[name = tensor("input_123_mode_0"), val = tensor("constant")]; tensor const_16_to_fp16 = const()[name = tensor("const_16_to_fp16"), val = tensor(0x0p+0)]; tensor input_123_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_123_mode_0, pad = input_123_pad_0, x = input_121_cast_fp16)[name = tensor("input_123_cast_fp16")]; tensor hidden_states_23_pad_type_0 = const()[name = tensor("hidden_states_23_pad_type_0"), val = tensor("valid")]; tensor hidden_states_23_strides_0 = const()[name = tensor("hidden_states_23_strides_0"), val = tensor([1, 1])]; tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_23_dilations_0 = const()[name = tensor("hidden_states_23_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_23_groups_0 = const()[name = tensor("hidden_states_23_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44643776)))]; tensor decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49362432)))]; tensor hidden_states_23_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; tensor var_344_cast_fp16 = add(x = var_302_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor("op_344_cast_fp16")]; tensor input_125_scale_factor_height_0 = const()[name = tensor("input_125_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_125_scale_factor_width_0 = const()[name = tensor("input_125_scale_factor_width_0"), val = tensor(0x1p+1)]; tensor input_125_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_344_cast_fp16)[name = tensor("input_125_cast_fp16")]; tensor var_352_begin_0 = const()[name = tensor("op_352_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_352_end_0 = const()[name = tensor("op_352_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_352_end_mask_0 = const()[name = tensor("op_352_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_352_cast_fp16 = slice_by_index(begin = var_352_begin_0, end = var_352_end_0, end_mask = var_352_end_mask_0, x = input_125_cast_fp16)[name = tensor("op_352_cast_fp16")]; tensor var_353_begin_0 = const()[name = tensor("op_353_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_353_end_0 = const()[name = tensor("op_353_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_353_end_mask_0 = const()[name = tensor("op_353_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_353_cast_fp16 = slice_by_index(begin = var_353_begin_0, end = var_353_end_0, end_mask = var_353_end_mask_0, x = input_125_cast_fp16)[name = tensor("op_353_cast_fp16")]; tensor input_127_interleave_0 = const()[name = tensor("input_127_interleave_0"), val = tensor(false)]; tensor input_127_cast_fp16 = concat(axis = var_24, interleave = input_127_interleave_0, values = (var_352_cast_fp16, input_125_cast_fp16, var_353_cast_fp16))[name = tensor("input_127_cast_fp16")]; tensor input_129_pad_0 = const()[name = tensor("input_129_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_129_mode_0 = const()[name = tensor("input_129_mode_0"), val = tensor("constant")]; tensor const_17_to_fp16 = const()[name = tensor("const_17_to_fp16"), val = tensor(0x0p+0)]; tensor input_129_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_129_mode_0, pad = input_129_pad_0, x = input_127_cast_fp16)[name = tensor("input_129_cast_fp16")]; tensor input_131_pad_type_0 = const()[name = tensor("input_131_pad_type_0"), val = tensor("valid")]; tensor input_131_strides_0 = const()[name = tensor("input_131_strides_0"), val = tensor([1, 1])]; tensor input_131_pad_0 = const()[name = tensor("input_131_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_131_dilations_0 = const()[name = tensor("input_131_dilations_0"), val = tensor([1, 1])]; tensor input_131_groups_0 = const()[name = tensor("input_131_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49363520)))]; tensor decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54082176)))]; tensor input_131_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("input_131_cast_fp16")]; tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_131_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54083264)))]; tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54084352)))]; tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; tensor input_135_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("input_135_cast_fp16")]; tensor var_381_begin_0 = const()[name = tensor("op_381_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_381_end_0 = const()[name = tensor("op_381_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_381_end_mask_0 = const()[name = tensor("op_381_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_381_cast_fp16 = slice_by_index(begin = var_381_begin_0, end = var_381_end_0, end_mask = var_381_end_mask_0, x = input_135_cast_fp16)[name = tensor("op_381_cast_fp16")]; tensor var_382_begin_0 = const()[name = tensor("op_382_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_382_end_0 = const()[name = tensor("op_382_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_382_end_mask_0 = const()[name = tensor("op_382_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_382_cast_fp16 = slice_by_index(begin = var_382_begin_0, end = var_382_end_0, end_mask = var_382_end_mask_0, x = input_135_cast_fp16)[name = tensor("op_382_cast_fp16")]; tensor input_137_interleave_0 = const()[name = tensor("input_137_interleave_0"), val = tensor(false)]; tensor input_137_cast_fp16 = concat(axis = var_24, interleave = input_137_interleave_0, values = (var_381_cast_fp16, input_135_cast_fp16, var_382_cast_fp16))[name = tensor("input_137_cast_fp16")]; tensor input_139_pad_0 = const()[name = tensor("input_139_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_139_mode_0 = const()[name = tensor("input_139_mode_0"), val = tensor("constant")]; tensor const_18_to_fp16 = const()[name = tensor("const_18_to_fp16"), val = tensor(0x0p+0)]; tensor input_139_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_139_mode_0, pad = input_139_pad_0, x = input_137_cast_fp16)[name = tensor("input_139_cast_fp16")]; tensor input_141_pad_type_0 = const()[name = tensor("input_141_pad_type_0"), val = tensor("valid")]; tensor input_141_strides_0 = const()[name = tensor("input_141_strides_0"), val = tensor([1, 1])]; tensor input_141_pad_0 = const()[name = tensor("input_141_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_141_dilations_0 = const()[name = tensor("input_141_dilations_0"), val = tensor([1, 1])]; tensor input_141_groups_0 = const()[name = tensor("input_141_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54085440)))]; tensor decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58804096)))]; tensor input_141_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("input_141_cast_fp16")]; tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_141_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58805184)))]; tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58806272)))]; tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; tensor input_145_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_145_cast_fp16")]; tensor var_399_begin_0 = const()[name = tensor("op_399_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_399_end_0 = const()[name = tensor("op_399_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_399_end_mask_0 = const()[name = tensor("op_399_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_399_cast_fp16 = slice_by_index(begin = var_399_begin_0, end = var_399_end_0, end_mask = var_399_end_mask_0, x = input_145_cast_fp16)[name = tensor("op_399_cast_fp16")]; tensor var_400_begin_0 = const()[name = tensor("op_400_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_400_end_0 = const()[name = tensor("op_400_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_400_end_mask_0 = const()[name = tensor("op_400_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_400_cast_fp16 = slice_by_index(begin = var_400_begin_0, end = var_400_end_0, end_mask = var_400_end_mask_0, x = input_145_cast_fp16)[name = tensor("op_400_cast_fp16")]; tensor input_149_interleave_0 = const()[name = tensor("input_149_interleave_0"), val = tensor(false)]; tensor input_149_cast_fp16 = concat(axis = var_24, interleave = input_149_interleave_0, values = (var_399_cast_fp16, input_145_cast_fp16, var_400_cast_fp16))[name = tensor("input_149_cast_fp16")]; tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_151_mode_0 = const()[name = tensor("input_151_mode_0"), val = tensor("constant")]; tensor const_19_to_fp16 = const()[name = tensor("const_19_to_fp16"), val = tensor(0x0p+0)]; tensor input_151_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("valid")]; tensor hidden_states_27_strides_0 = const()[name = tensor("hidden_states_27_strides_0"), val = tensor([1, 1])]; tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_27_dilations_0 = const()[name = tensor("hidden_states_27_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_27_groups_0 = const()[name = tensor("hidden_states_27_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58807360)))]; tensor decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63526016)))]; tensor hidden_states_27_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_151_cast_fp16)[name = tensor("hidden_states_27_cast_fp16")]; tensor var_410_cast_fp16 = add(x = input_131_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor("op_410_cast_fp16")]; tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_410_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63527104)))]; tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63528192)))]; tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; tensor input_157_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_157_cast_fp16")]; tensor var_423_begin_0 = const()[name = tensor("op_423_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_423_end_0 = const()[name = tensor("op_423_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_423_end_mask_0 = const()[name = tensor("op_423_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_423_cast_fp16 = slice_by_index(begin = var_423_begin_0, end = var_423_end_0, end_mask = var_423_end_mask_0, x = input_157_cast_fp16)[name = tensor("op_423_cast_fp16")]; tensor var_424_begin_0 = const()[name = tensor("op_424_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_424_end_0 = const()[name = tensor("op_424_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_424_end_mask_0 = const()[name = tensor("op_424_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_424_cast_fp16 = slice_by_index(begin = var_424_begin_0, end = var_424_end_0, end_mask = var_424_end_mask_0, x = input_157_cast_fp16)[name = tensor("op_424_cast_fp16")]; tensor input_159_interleave_0 = const()[name = tensor("input_159_interleave_0"), val = tensor(false)]; tensor input_159_cast_fp16 = concat(axis = var_24, interleave = input_159_interleave_0, values = (var_423_cast_fp16, input_157_cast_fp16, var_424_cast_fp16))[name = tensor("input_159_cast_fp16")]; tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_161_mode_0 = const()[name = tensor("input_161_mode_0"), val = tensor("constant")]; tensor const_20_to_fp16 = const()[name = tensor("const_20_to_fp16"), val = tensor(0x0p+0)]; tensor input_161_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_161_mode_0, pad = input_161_pad_0, x = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; tensor input_163_pad_type_0 = const()[name = tensor("input_163_pad_type_0"), val = tensor("valid")]; tensor input_163_strides_0 = const()[name = tensor("input_163_strides_0"), val = tensor([1, 1])]; tensor input_163_pad_0 = const()[name = tensor("input_163_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_163_dilations_0 = const()[name = tensor("input_163_dilations_0"), val = tensor([1, 1])]; tensor input_163_groups_0 = const()[name = tensor("input_163_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63529280)))]; tensor decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68247936)))]; tensor input_163_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("input_163_cast_fp16")]; tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_163_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68249024)))]; tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68250112)))]; tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; tensor input_167_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("input_167_cast_fp16")]; tensor var_441_begin_0 = const()[name = tensor("op_441_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_441_end_0 = const()[name = tensor("op_441_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_441_end_mask_0 = const()[name = tensor("op_441_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_441_cast_fp16 = slice_by_index(begin = var_441_begin_0, end = var_441_end_0, end_mask = var_441_end_mask_0, x = input_167_cast_fp16)[name = tensor("op_441_cast_fp16")]; tensor var_442_begin_0 = const()[name = tensor("op_442_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_442_end_0 = const()[name = tensor("op_442_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_442_end_mask_0 = const()[name = tensor("op_442_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_442_cast_fp16 = slice_by_index(begin = var_442_begin_0, end = var_442_end_0, end_mask = var_442_end_mask_0, x = input_167_cast_fp16)[name = tensor("op_442_cast_fp16")]; tensor input_171_interleave_0 = const()[name = tensor("input_171_interleave_0"), val = tensor(false)]; tensor input_171_cast_fp16 = concat(axis = var_24, interleave = input_171_interleave_0, values = (var_441_cast_fp16, input_167_cast_fp16, var_442_cast_fp16))[name = tensor("input_171_cast_fp16")]; tensor input_173_pad_0 = const()[name = tensor("input_173_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_173_mode_0 = const()[name = tensor("input_173_mode_0"), val = tensor("constant")]; tensor const_21_to_fp16 = const()[name = tensor("const_21_to_fp16"), val = tensor(0x0p+0)]; tensor input_173_cast_fp16 = pad(constant_val = const_21_to_fp16, mode = input_173_mode_0, pad = input_173_pad_0, x = input_171_cast_fp16)[name = tensor("input_173_cast_fp16")]; tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("valid")]; tensor hidden_states_29_strides_0 = const()[name = tensor("hidden_states_29_strides_0"), val = tensor([1, 1])]; tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_29_dilations_0 = const()[name = tensor("hidden_states_29_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_29_groups_0 = const()[name = tensor("hidden_states_29_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68251200)))]; tensor decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72969856)))]; tensor hidden_states_29_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("hidden_states_29_cast_fp16")]; tensor var_452_cast_fp16 = add(x = var_410_cast_fp16, y = hidden_states_29_cast_fp16)[name = tensor("op_452_cast_fp16")]; tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_452_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72970944)))]; tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72972032)))]; tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; tensor input_179_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("input_179_cast_fp16")]; tensor var_465_begin_0 = const()[name = tensor("op_465_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_465_end_0 = const()[name = tensor("op_465_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_465_end_mask_0 = const()[name = tensor("op_465_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_465_cast_fp16 = slice_by_index(begin = var_465_begin_0, end = var_465_end_0, end_mask = var_465_end_mask_0, x = input_179_cast_fp16)[name = tensor("op_465_cast_fp16")]; tensor var_466_begin_0 = const()[name = tensor("op_466_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_466_end_0 = const()[name = tensor("op_466_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_466_end_mask_0 = const()[name = tensor("op_466_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_466_cast_fp16 = slice_by_index(begin = var_466_begin_0, end = var_466_end_0, end_mask = var_466_end_mask_0, x = input_179_cast_fp16)[name = tensor("op_466_cast_fp16")]; tensor input_181_interleave_0 = const()[name = tensor("input_181_interleave_0"), val = tensor(false)]; tensor input_181_cast_fp16 = concat(axis = var_24, interleave = input_181_interleave_0, values = (var_465_cast_fp16, input_179_cast_fp16, var_466_cast_fp16))[name = tensor("input_181_cast_fp16")]; tensor input_183_pad_0 = const()[name = tensor("input_183_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_183_mode_0 = const()[name = tensor("input_183_mode_0"), val = tensor("constant")]; tensor const_22_to_fp16 = const()[name = tensor("const_22_to_fp16"), val = tensor(0x0p+0)]; tensor input_183_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_183_mode_0, pad = input_183_pad_0, x = input_181_cast_fp16)[name = tensor("input_183_cast_fp16")]; tensor input_185_pad_type_0 = const()[name = tensor("input_185_pad_type_0"), val = tensor("valid")]; tensor input_185_strides_0 = const()[name = tensor("input_185_strides_0"), val = tensor([1, 1])]; tensor input_185_pad_0 = const()[name = tensor("input_185_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_185_dilations_0 = const()[name = tensor("input_185_dilations_0"), val = tensor([1, 1])]; tensor input_185_groups_0 = const()[name = tensor("input_185_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72973120)))]; tensor decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77691776)))]; tensor input_185_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_183_cast_fp16)[name = tensor("input_185_cast_fp16")]; tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 128, 256])]; tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_185_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 128, 256])]; tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77692864)))]; tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77693952)))]; tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; tensor input_189_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_189_cast_fp16")]; tensor var_483_begin_0 = const()[name = tensor("op_483_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_483_end_0 = const()[name = tensor("op_483_end_0"), val = tensor([1, 512, 128, 256])]; tensor var_483_end_mask_0 = const()[name = tensor("op_483_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_483_cast_fp16 = slice_by_index(begin = var_483_begin_0, end = var_483_end_0, end_mask = var_483_end_mask_0, x = input_189_cast_fp16)[name = tensor("op_483_cast_fp16")]; tensor var_484_begin_0 = const()[name = tensor("op_484_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_484_end_0 = const()[name = tensor("op_484_end_0"), val = tensor([1, 512, 128, 1])]; tensor var_484_end_mask_0 = const()[name = tensor("op_484_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_484_cast_fp16 = slice_by_index(begin = var_484_begin_0, end = var_484_end_0, end_mask = var_484_end_mask_0, x = input_189_cast_fp16)[name = tensor("op_484_cast_fp16")]; tensor input_193_interleave_0 = const()[name = tensor("input_193_interleave_0"), val = tensor(false)]; tensor input_193_cast_fp16 = concat(axis = var_24, interleave = input_193_interleave_0, values = (var_483_cast_fp16, input_189_cast_fp16, var_484_cast_fp16))[name = tensor("input_193_cast_fp16")]; tensor input_195_pad_0 = const()[name = tensor("input_195_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_195_mode_0 = const()[name = tensor("input_195_mode_0"), val = tensor("constant")]; tensor const_23_to_fp16 = const()[name = tensor("const_23_to_fp16"), val = tensor(0x0p+0)]; tensor input_195_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_195_mode_0, pad = input_195_pad_0, x = input_193_cast_fp16)[name = tensor("input_195_cast_fp16")]; tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("valid")]; tensor hidden_states_31_strides_0 = const()[name = tensor("hidden_states_31_strides_0"), val = tensor([1, 1])]; tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_31_dilations_0 = const()[name = tensor("hidden_states_31_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_31_groups_0 = const()[name = tensor("hidden_states_31_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77695040)))]; tensor decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82413696)))]; tensor hidden_states_31_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_31_dilations_0, groups = hidden_states_31_groups_0, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = hidden_states_31_strides_0, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; tensor var_494_cast_fp16 = add(x = var_452_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor("op_494_cast_fp16")]; tensor input_197_scale_factor_height_0 = const()[name = tensor("input_197_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_197_scale_factor_width_0 = const()[name = tensor("input_197_scale_factor_width_0"), val = tensor(0x1p+1)]; tensor input_197_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_197_scale_factor_height_0, scale_factor_width = input_197_scale_factor_width_0, x = var_494_cast_fp16)[name = tensor("input_197_cast_fp16")]; tensor var_502_begin_0 = const()[name = tensor("op_502_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_502_end_0 = const()[name = tensor("op_502_end_0"), val = tensor([1, 512, 256, 512])]; tensor var_502_end_mask_0 = const()[name = tensor("op_502_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_502_cast_fp16 = slice_by_index(begin = var_502_begin_0, end = var_502_end_0, end_mask = var_502_end_mask_0, x = input_197_cast_fp16)[name = tensor("op_502_cast_fp16")]; tensor var_503_begin_0 = const()[name = tensor("op_503_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_503_end_0 = const()[name = tensor("op_503_end_0"), val = tensor([1, 512, 256, 1])]; tensor var_503_end_mask_0 = const()[name = tensor("op_503_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_503_cast_fp16 = slice_by_index(begin = var_503_begin_0, end = var_503_end_0, end_mask = var_503_end_mask_0, x = input_197_cast_fp16)[name = tensor("op_503_cast_fp16")]; tensor input_199_interleave_0 = const()[name = tensor("input_199_interleave_0"), val = tensor(false)]; tensor input_199_cast_fp16 = concat(axis = var_24, interleave = input_199_interleave_0, values = (var_502_cast_fp16, input_197_cast_fp16, var_503_cast_fp16))[name = tensor("input_199_cast_fp16")]; tensor input_201_pad_0 = const()[name = tensor("input_201_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_201_mode_0 = const()[name = tensor("input_201_mode_0"), val = tensor("constant")]; tensor const_24_to_fp16 = const()[name = tensor("const_24_to_fp16"), val = tensor(0x0p+0)]; tensor input_201_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = tensor("input_201_cast_fp16")]; tensor input_203_pad_type_0 = const()[name = tensor("input_203_pad_type_0"), val = tensor("valid")]; tensor input_203_strides_0 = const()[name = tensor("input_203_strides_0"), val = tensor([1, 1])]; tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_203_dilations_0 = const()[name = tensor("input_203_dilations_0"), val = tensor([1, 1])]; tensor input_203_groups_0 = const()[name = tensor("input_203_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82414784)))]; tensor decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87133440)))]; tensor input_203_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_201_cast_fp16)[name = tensor("input_203_cast_fp16")]; tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 256, 512])]; tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_203_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 256, 512])]; tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87134528)))]; tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87135616)))]; tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; tensor input_207_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("input_207_cast_fp16")]; tensor var_532_begin_0 = const()[name = tensor("op_532_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_532_end_0 = const()[name = tensor("op_532_end_0"), val = tensor([1, 512, 256, 512])]; tensor var_532_end_mask_0 = const()[name = tensor("op_532_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_532_cast_fp16 = slice_by_index(begin = var_532_begin_0, end = var_532_end_0, end_mask = var_532_end_mask_0, x = input_207_cast_fp16)[name = tensor("op_532_cast_fp16")]; tensor var_533_begin_0 = const()[name = tensor("op_533_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_533_end_0 = const()[name = tensor("op_533_end_0"), val = tensor([1, 512, 256, 1])]; tensor var_533_end_mask_0 = const()[name = tensor("op_533_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_533_cast_fp16 = slice_by_index(begin = var_533_begin_0, end = var_533_end_0, end_mask = var_533_end_mask_0, x = input_207_cast_fp16)[name = tensor("op_533_cast_fp16")]; tensor input_209_interleave_0 = const()[name = tensor("input_209_interleave_0"), val = tensor(false)]; tensor input_209_cast_fp16 = concat(axis = var_24, interleave = input_209_interleave_0, values = (var_532_cast_fp16, input_207_cast_fp16, var_533_cast_fp16))[name = tensor("input_209_cast_fp16")]; tensor input_211_pad_0 = const()[name = tensor("input_211_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_211_mode_0 = const()[name = tensor("input_211_mode_0"), val = tensor("constant")]; tensor const_25_to_fp16 = const()[name = tensor("const_25_to_fp16"), val = tensor(0x0p+0)]; tensor input_211_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = tensor("input_211_cast_fp16")]; tensor input_213_pad_type_0 = const()[name = tensor("input_213_pad_type_0"), val = tensor("valid")]; tensor input_213_strides_0 = const()[name = tensor("input_213_strides_0"), val = tensor([1, 1])]; tensor input_213_pad_0 = const()[name = tensor("input_213_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_213_dilations_0 = const()[name = tensor("input_213_dilations_0"), val = tensor([1, 1])]; tensor input_213_groups_0 = const()[name = tensor("input_213_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87136704)))]; tensor decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496064)))]; tensor input_213_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 256, 512])]; tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_213_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 256, 512])]; tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; tensor add_37_mean_0_to_fp16 = const()[name = tensor("add_37_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496640)))]; tensor add_37_variance_0_to_fp16 = const()[name = tensor("add_37_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497216)))]; tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497792)))]; tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498368)))]; tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; tensor input_217_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_217_cast_fp16")]; tensor var_550_begin_0 = const()[name = tensor("op_550_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_550_end_0 = const()[name = tensor("op_550_end_0"), val = tensor([1, 256, 256, 512])]; tensor var_550_end_mask_0 = const()[name = tensor("op_550_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_550_cast_fp16 = slice_by_index(begin = var_550_begin_0, end = var_550_end_0, end_mask = var_550_end_mask_0, x = input_217_cast_fp16)[name = tensor("op_550_cast_fp16")]; tensor var_551_begin_0 = const()[name = tensor("op_551_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_551_end_0 = const()[name = tensor("op_551_end_0"), val = tensor([1, 256, 256, 1])]; tensor var_551_end_mask_0 = const()[name = tensor("op_551_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_551_cast_fp16 = slice_by_index(begin = var_551_begin_0, end = var_551_end_0, end_mask = var_551_end_mask_0, x = input_217_cast_fp16)[name = tensor("op_551_cast_fp16")]; tensor input_221_interleave_0 = const()[name = tensor("input_221_interleave_0"), val = tensor(false)]; tensor input_221_cast_fp16 = concat(axis = var_24, interleave = input_221_interleave_0, values = (var_550_cast_fp16, input_217_cast_fp16, var_551_cast_fp16))[name = tensor("input_221_cast_fp16")]; tensor input_223_pad_0 = const()[name = tensor("input_223_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_223_mode_0 = const()[name = tensor("input_223_mode_0"), val = tensor("constant")]; tensor const_26_to_fp16 = const()[name = tensor("const_26_to_fp16"), val = tensor(0x0p+0)]; tensor input_223_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_223_mode_0, pad = input_223_pad_0, x = input_221_cast_fp16)[name = tensor("input_223_cast_fp16")]; tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("valid")]; tensor hidden_states_35_strides_0 = const()[name = tensor("hidden_states_35_strides_0"), val = tensor([1, 1])]; tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_35_dilations_0 = const()[name = tensor("hidden_states_35_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_35_groups_0 = const()[name = tensor("hidden_states_35_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498944)))]; tensor decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90678656)))]; tensor hidden_states_35_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_223_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("valid")]; tensor input_tensor_1_strides_0 = const()[name = tensor("input_tensor_1_strides_0"), val = tensor([1, 1])]; tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_tensor_1_dilations_0 = const()[name = tensor("input_tensor_1_dilations_0"), val = tensor([1, 1])]; tensor input_tensor_1_groups_0 = const()[name = tensor("input_tensor_1_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90679232)))]; tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90941440)))]; tensor input_tensor_1_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_1_dilations_0, groups = input_tensor_1_groups_0, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = input_tensor_1_strides_0, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_203_cast_fp16)[name = tensor("input_tensor_1_cast_fp16")]; tensor var_568_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor("op_568_cast_fp16")]; tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 256, 512])]; tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_568_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 256, 512])]; tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942016)))]; tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942592)))]; tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; tensor input_229_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_229_cast_fp16")]; tensor var_581_begin_0 = const()[name = tensor("op_581_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_581_end_0 = const()[name = tensor("op_581_end_0"), val = tensor([1, 256, 256, 512])]; tensor var_581_end_mask_0 = const()[name = tensor("op_581_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_581_cast_fp16 = slice_by_index(begin = var_581_begin_0, end = var_581_end_0, end_mask = var_581_end_mask_0, x = input_229_cast_fp16)[name = tensor("op_581_cast_fp16")]; tensor var_582_begin_0 = const()[name = tensor("op_582_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_582_end_0 = const()[name = tensor("op_582_end_0"), val = tensor([1, 256, 256, 1])]; tensor var_582_end_mask_0 = const()[name = tensor("op_582_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = input_229_cast_fp16)[name = tensor("op_582_cast_fp16")]; tensor input_231_interleave_0 = const()[name = tensor("input_231_interleave_0"), val = tensor(false)]; tensor input_231_cast_fp16 = concat(axis = var_24, interleave = input_231_interleave_0, values = (var_581_cast_fp16, input_229_cast_fp16, var_582_cast_fp16))[name = tensor("input_231_cast_fp16")]; tensor input_233_pad_0 = const()[name = tensor("input_233_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_233_mode_0 = const()[name = tensor("input_233_mode_0"), val = tensor("constant")]; tensor const_27_to_fp16 = const()[name = tensor("const_27_to_fp16"), val = tensor(0x0p+0)]; tensor input_233_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_233_mode_0, pad = input_233_pad_0, x = input_231_cast_fp16)[name = tensor("input_233_cast_fp16")]; tensor input_235_pad_type_0 = const()[name = tensor("input_235_pad_type_0"), val = tensor("valid")]; tensor input_235_strides_0 = const()[name = tensor("input_235_strides_0"), val = tensor([1, 1])]; tensor input_235_pad_0 = const()[name = tensor("input_235_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_235_dilations_0 = const()[name = tensor("input_235_dilations_0"), val = tensor([1, 1])]; tensor input_235_groups_0 = const()[name = tensor("input_235_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90943168)))]; tensor decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92122880)))]; tensor input_235_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = input_235_dilations_0, groups = input_235_groups_0, pad = input_235_pad_0, pad_type = input_235_pad_type_0, strides = input_235_strides_0, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("input_235_cast_fp16")]; tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 256, 512])]; tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_235_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 256, 512])]; tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92123456)))]; tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124032)))]; tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; tensor input_239_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_239_cast_fp16")]; tensor var_599_begin_0 = const()[name = tensor("op_599_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_599_end_0 = const()[name = tensor("op_599_end_0"), val = tensor([1, 256, 256, 512])]; tensor var_599_end_mask_0 = const()[name = tensor("op_599_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_599_cast_fp16 = slice_by_index(begin = var_599_begin_0, end = var_599_end_0, end_mask = var_599_end_mask_0, x = input_239_cast_fp16)[name = tensor("op_599_cast_fp16")]; tensor var_600_begin_0 = const()[name = tensor("op_600_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_600_end_0 = const()[name = tensor("op_600_end_0"), val = tensor([1, 256, 256, 1])]; tensor var_600_end_mask_0 = const()[name = tensor("op_600_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_600_cast_fp16 = slice_by_index(begin = var_600_begin_0, end = var_600_end_0, end_mask = var_600_end_mask_0, x = input_239_cast_fp16)[name = tensor("op_600_cast_fp16")]; tensor input_243_interleave_0 = const()[name = tensor("input_243_interleave_0"), val = tensor(false)]; tensor input_243_cast_fp16 = concat(axis = var_24, interleave = input_243_interleave_0, values = (var_599_cast_fp16, input_239_cast_fp16, var_600_cast_fp16))[name = tensor("input_243_cast_fp16")]; tensor input_245_pad_0 = const()[name = tensor("input_245_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_245_mode_0 = const()[name = tensor("input_245_mode_0"), val = tensor("constant")]; tensor const_28_to_fp16 = const()[name = tensor("const_28_to_fp16"), val = tensor(0x0p+0)]; tensor input_245_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_245_mode_0, pad = input_245_pad_0, x = input_243_cast_fp16)[name = tensor("input_245_cast_fp16")]; tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("valid")]; tensor hidden_states_37_strides_0 = const()[name = tensor("hidden_states_37_strides_0"), val = tensor([1, 1])]; tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_37_dilations_0 = const()[name = tensor("hidden_states_37_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_37_groups_0 = const()[name = tensor("hidden_states_37_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124608)))]; tensor decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304320)))]; tensor hidden_states_37_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_245_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; tensor var_610_cast_fp16 = add(x = var_568_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("op_610_cast_fp16")]; tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 256, 512])]; tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_610_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 256, 512])]; tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304896)))]; tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93305472)))]; tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; tensor input_251_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_251_cast_fp16")]; tensor var_623_begin_0 = const()[name = tensor("op_623_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_623_end_0 = const()[name = tensor("op_623_end_0"), val = tensor([1, 256, 256, 512])]; tensor var_623_end_mask_0 = const()[name = tensor("op_623_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_623_cast_fp16 = slice_by_index(begin = var_623_begin_0, end = var_623_end_0, end_mask = var_623_end_mask_0, x = input_251_cast_fp16)[name = tensor("op_623_cast_fp16")]; tensor var_624_begin_0 = const()[name = tensor("op_624_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_624_end_0 = const()[name = tensor("op_624_end_0"), val = tensor([1, 256, 256, 1])]; tensor var_624_end_mask_0 = const()[name = tensor("op_624_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_624_cast_fp16 = slice_by_index(begin = var_624_begin_0, end = var_624_end_0, end_mask = var_624_end_mask_0, x = input_251_cast_fp16)[name = tensor("op_624_cast_fp16")]; tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; tensor input_253_cast_fp16 = concat(axis = var_24, interleave = input_253_interleave_0, values = (var_623_cast_fp16, input_251_cast_fp16, var_624_cast_fp16))[name = tensor("input_253_cast_fp16")]; tensor input_255_pad_0 = const()[name = tensor("input_255_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_255_mode_0 = const()[name = tensor("input_255_mode_0"), val = tensor("constant")]; tensor const_29_to_fp16 = const()[name = tensor("const_29_to_fp16"), val = tensor(0x0p+0)]; tensor input_255_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor("input_255_cast_fp16")]; tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1, 1])]; tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1, 1])]; tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93306048)))]; tensor decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94485760)))]; tensor input_257_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_255_cast_fp16)[name = tensor("input_257_cast_fp16")]; tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 256, 512])]; tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_257_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 256, 512])]; tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486336)))]; tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486912)))]; tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; tensor input_261_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_261_cast_fp16")]; tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 256, 256, 512])]; tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_641_cast_fp16 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, x = input_261_cast_fp16)[name = tensor("op_641_cast_fp16")]; tensor var_642_begin_0 = const()[name = tensor("op_642_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_642_end_0 = const()[name = tensor("op_642_end_0"), val = tensor([1, 256, 256, 1])]; tensor var_642_end_mask_0 = const()[name = tensor("op_642_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_642_cast_fp16 = slice_by_index(begin = var_642_begin_0, end = var_642_end_0, end_mask = var_642_end_mask_0, x = input_261_cast_fp16)[name = tensor("op_642_cast_fp16")]; tensor input_265_interleave_0 = const()[name = tensor("input_265_interleave_0"), val = tensor(false)]; tensor input_265_cast_fp16 = concat(axis = var_24, interleave = input_265_interleave_0, values = (var_641_cast_fp16, input_261_cast_fp16, var_642_cast_fp16))[name = tensor("input_265_cast_fp16")]; tensor input_267_pad_0 = const()[name = tensor("input_267_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_267_mode_0 = const()[name = tensor("input_267_mode_0"), val = tensor("constant")]; tensor const_30_to_fp16 = const()[name = tensor("const_30_to_fp16"), val = tensor(0x0p+0)]; tensor input_267_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_267_mode_0, pad = input_267_pad_0, x = input_265_cast_fp16)[name = tensor("input_267_cast_fp16")]; tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("valid")]; tensor hidden_states_39_strides_0 = const()[name = tensor("hidden_states_39_strides_0"), val = tensor([1, 1])]; tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_39_dilations_0 = const()[name = tensor("hidden_states_39_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_39_groups_0 = const()[name = tensor("hidden_states_39_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94487488)))]; tensor decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667200)))]; tensor hidden_states_39_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_39_dilations_0, groups = hidden_states_39_groups_0, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = hidden_states_39_strides_0, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; tensor var_652_cast_fp16 = add(x = var_610_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor("op_652_cast_fp16")]; tensor input_269_scale_factor_height_0 = const()[name = tensor("input_269_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_269_scale_factor_width_0 = const()[name = tensor("input_269_scale_factor_width_0"), val = tensor(0x1p+1)]; tensor input_269_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_269_scale_factor_height_0, scale_factor_width = input_269_scale_factor_width_0, x = var_652_cast_fp16)[name = tensor("input_269_cast_fp16")]; tensor var_660_begin_0 = const()[name = tensor("op_660_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_660_end_0 = const()[name = tensor("op_660_end_0"), val = tensor([1, 256, 512, 1024])]; tensor var_660_end_mask_0 = const()[name = tensor("op_660_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_660_cast_fp16 = slice_by_index(begin = var_660_begin_0, end = var_660_end_0, end_mask = var_660_end_mask_0, x = input_269_cast_fp16)[name = tensor("op_660_cast_fp16")]; tensor var_661_begin_0 = const()[name = tensor("op_661_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_661_end_0 = const()[name = tensor("op_661_end_0"), val = tensor([1, 256, 512, 1])]; tensor var_661_end_mask_0 = const()[name = tensor("op_661_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_661_cast_fp16 = slice_by_index(begin = var_661_begin_0, end = var_661_end_0, end_mask = var_661_end_mask_0, x = input_269_cast_fp16)[name = tensor("op_661_cast_fp16")]; tensor input_271_interleave_0 = const()[name = tensor("input_271_interleave_0"), val = tensor(false)]; tensor input_271_cast_fp16 = concat(axis = var_24, interleave = input_271_interleave_0, values = (var_660_cast_fp16, input_269_cast_fp16, var_661_cast_fp16))[name = tensor("input_271_cast_fp16")]; tensor input_273_pad_0 = const()[name = tensor("input_273_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_273_mode_0 = const()[name = tensor("input_273_mode_0"), val = tensor("constant")]; tensor const_31_to_fp16 = const()[name = tensor("const_31_to_fp16"), val = tensor(0x0p+0)]; tensor input_273_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_273_mode_0, pad = input_273_pad_0, x = input_271_cast_fp16)[name = tensor("input_273_cast_fp16")]; tensor input_275_pad_type_0 = const()[name = tensor("input_275_pad_type_0"), val = tensor("valid")]; tensor input_275_strides_0 = const()[name = tensor("input_275_strides_0"), val = tensor([1, 1])]; tensor input_275_pad_0 = const()[name = tensor("input_275_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_275_dilations_0 = const()[name = tensor("input_275_dilations_0"), val = tensor([1, 1])]; tensor input_275_groups_0 = const()[name = tensor("input_275_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667776)))]; tensor decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96847488)))]; tensor input_275_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = input_275_dilations_0, groups = input_275_groups_0, pad = input_275_pad_0, pad_type = input_275_pad_type_0, strides = input_275_strides_0, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_273_cast_fp16)[name = tensor("input_275_cast_fp16")]; tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 512, 1024])]; tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_275_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 512, 1024])]; tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848064)))]; tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848640)))]; tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; tensor input_279_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("input_279_cast_fp16")]; tensor var_688_begin_0 = const()[name = tensor("op_688_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_688_end_0 = const()[name = tensor("op_688_end_0"), val = tensor([1, 256, 512, 1024])]; tensor var_688_end_mask_0 = const()[name = tensor("op_688_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_688_cast_fp16 = slice_by_index(begin = var_688_begin_0, end = var_688_end_0, end_mask = var_688_end_mask_0, x = input_279_cast_fp16)[name = tensor("op_688_cast_fp16")]; tensor var_689_begin_0 = const()[name = tensor("op_689_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_689_end_0 = const()[name = tensor("op_689_end_0"), val = tensor([1, 256, 512, 1])]; tensor var_689_end_mask_0 = const()[name = tensor("op_689_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_689_cast_fp16 = slice_by_index(begin = var_689_begin_0, end = var_689_end_0, end_mask = var_689_end_mask_0, x = input_279_cast_fp16)[name = tensor("op_689_cast_fp16")]; tensor input_281_interleave_0 = const()[name = tensor("input_281_interleave_0"), val = tensor(false)]; tensor input_281_cast_fp16 = concat(axis = var_24, interleave = input_281_interleave_0, values = (var_688_cast_fp16, input_279_cast_fp16, var_689_cast_fp16))[name = tensor("input_281_cast_fp16")]; tensor input_283_pad_0 = const()[name = tensor("input_283_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_283_mode_0 = const()[name = tensor("input_283_mode_0"), val = tensor("constant")]; tensor const_32_to_fp16 = const()[name = tensor("const_32_to_fp16"), val = tensor(0x0p+0)]; tensor input_283_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_283_mode_0, pad = input_283_pad_0, x = input_281_cast_fp16)[name = tensor("input_283_cast_fp16")]; tensor input_285_pad_type_0 = const()[name = tensor("input_285_pad_type_0"), val = tensor("valid")]; tensor input_285_strides_0 = const()[name = tensor("input_285_strides_0"), val = tensor([1, 1])]; tensor input_285_pad_0 = const()[name = tensor("input_285_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_285_dilations_0 = const()[name = tensor("input_285_dilations_0"), val = tensor([1, 1])]; tensor input_285_groups_0 = const()[name = tensor("input_285_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96849216)))]; tensor decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439104)))]; tensor input_285_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("input_285_cast_fp16")]; tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_285_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; tensor add_49_mean_0_to_fp16 = const()[name = tensor("add_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439424)))]; tensor add_49_variance_0_to_fp16 = const()[name = tensor("add_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439744)))]; tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440064)))]; tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440384)))]; tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; tensor input_289_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor("input_289_cast_fp16")]; tensor var_706_begin_0 = const()[name = tensor("op_706_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_706_end_0 = const()[name = tensor("op_706_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_706_end_mask_0 = const()[name = tensor("op_706_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_706_cast_fp16 = slice_by_index(begin = var_706_begin_0, end = var_706_end_0, end_mask = var_706_end_mask_0, x = input_289_cast_fp16)[name = tensor("op_706_cast_fp16")]; tensor var_707_begin_0 = const()[name = tensor("op_707_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_707_end_0 = const()[name = tensor("op_707_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_707_end_mask_0 = const()[name = tensor("op_707_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_707_cast_fp16 = slice_by_index(begin = var_707_begin_0, end = var_707_end_0, end_mask = var_707_end_mask_0, x = input_289_cast_fp16)[name = tensor("op_707_cast_fp16")]; tensor input_293_interleave_0 = const()[name = tensor("input_293_interleave_0"), val = tensor(false)]; tensor input_293_cast_fp16 = concat(axis = var_24, interleave = input_293_interleave_0, values = (var_706_cast_fp16, input_289_cast_fp16, var_707_cast_fp16))[name = tensor("input_293_cast_fp16")]; tensor input_295_pad_0 = const()[name = tensor("input_295_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_295_mode_0 = const()[name = tensor("input_295_mode_0"), val = tensor("constant")]; tensor const_33_to_fp16 = const()[name = tensor("const_33_to_fp16"), val = tensor(0x0p+0)]; tensor input_295_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = tensor("input_295_cast_fp16")]; tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("valid")]; tensor hidden_states_43_strides_0 = const()[name = tensor("hidden_states_43_strides_0"), val = tensor([1, 1])]; tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_43_dilations_0 = const()[name = tensor("hidden_states_43_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_43_groups_0 = const()[name = tensor("hidden_states_43_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440704)))]; tensor decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97735680)))]; tensor hidden_states_43_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_295_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("valid")]; tensor input_tensor_strides_0 = const()[name = tensor("input_tensor_strides_0"), val = tensor([1, 1])]; tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_tensor_dilations_0 = const()[name = tensor("input_tensor_dilations_0"), val = tensor([1, 1])]; tensor input_tensor_groups_0 = const()[name = tensor("input_tensor_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97736000)))]; tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801600)))]; tensor input_tensor_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_dilations_0, groups = input_tensor_groups_0, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = input_tensor_strides_0, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_275_cast_fp16)[name = tensor("input_tensor_cast_fp16")]; tensor var_724_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor("op_724_cast_fp16")]; tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_724_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801920)))]; tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802240)))]; tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; tensor input_301_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_301_cast_fp16")]; tensor var_737_begin_0 = const()[name = tensor("op_737_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_737_end_0 = const()[name = tensor("op_737_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_737_end_mask_0 = const()[name = tensor("op_737_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = input_301_cast_fp16)[name = tensor("op_737_cast_fp16")]; tensor var_738_begin_0 = const()[name = tensor("op_738_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_738_end_0 = const()[name = tensor("op_738_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_738_end_mask_0 = const()[name = tensor("op_738_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_738_cast_fp16 = slice_by_index(begin = var_738_begin_0, end = var_738_end_0, end_mask = var_738_end_mask_0, x = input_301_cast_fp16)[name = tensor("op_738_cast_fp16")]; tensor input_303_interleave_0 = const()[name = tensor("input_303_interleave_0"), val = tensor(false)]; tensor input_303_cast_fp16 = concat(axis = var_24, interleave = input_303_interleave_0, values = (var_737_cast_fp16, input_301_cast_fp16, var_738_cast_fp16))[name = tensor("input_303_cast_fp16")]; tensor input_305_pad_0 = const()[name = tensor("input_305_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_305_mode_0 = const()[name = tensor("input_305_mode_0"), val = tensor("constant")]; tensor const_34_to_fp16 = const()[name = tensor("const_34_to_fp16"), val = tensor(0x0p+0)]; tensor input_305_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = input_305_mode_0, pad = input_305_pad_0, x = input_303_cast_fp16)[name = tensor("input_305_cast_fp16")]; tensor input_307_pad_type_0 = const()[name = tensor("input_307_pad_type_0"), val = tensor("valid")]; tensor input_307_strides_0 = const()[name = tensor("input_307_strides_0"), val = tensor([1, 1])]; tensor input_307_pad_0 = const()[name = tensor("input_307_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_307_dilations_0 = const()[name = tensor("input_307_dilations_0"), val = tensor([1, 1])]; tensor input_307_groups_0 = const()[name = tensor("input_307_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802560)))]; tensor decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097536)))]; tensor input_307_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = input_307_dilations_0, groups = input_307_groups_0, pad = input_307_pad_0, pad_type = input_307_pad_type_0, strides = input_307_strides_0, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("input_307_cast_fp16")]; tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_307_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097856)))]; tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098176)))]; tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; tensor input_311_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_311_cast_fp16")]; tensor var_755_begin_0 = const()[name = tensor("op_755_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_755_end_0 = const()[name = tensor("op_755_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_755_end_mask_0 = const()[name = tensor("op_755_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_755_cast_fp16 = slice_by_index(begin = var_755_begin_0, end = var_755_end_0, end_mask = var_755_end_mask_0, x = input_311_cast_fp16)[name = tensor("op_755_cast_fp16")]; tensor var_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = input_311_cast_fp16)[name = tensor("op_756_cast_fp16")]; tensor input_315_interleave_0 = const()[name = tensor("input_315_interleave_0"), val = tensor(false)]; tensor input_315_cast_fp16 = concat(axis = var_24, interleave = input_315_interleave_0, values = (var_755_cast_fp16, input_311_cast_fp16, var_756_cast_fp16))[name = tensor("input_315_cast_fp16")]; tensor input_317_pad_0 = const()[name = tensor("input_317_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_317_mode_0 = const()[name = tensor("input_317_mode_0"), val = tensor("constant")]; tensor const_35_to_fp16 = const()[name = tensor("const_35_to_fp16"), val = tensor(0x0p+0)]; tensor input_317_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_317_mode_0, pad = input_317_pad_0, x = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; tensor hidden_states_45_pad_type_0 = const()[name = tensor("hidden_states_45_pad_type_0"), val = tensor("valid")]; tensor hidden_states_45_strides_0 = const()[name = tensor("hidden_states_45_strides_0"), val = tensor([1, 1])]; tensor hidden_states_45_pad_0 = const()[name = tensor("hidden_states_45_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_45_dilations_0 = const()[name = tensor("hidden_states_45_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_45_groups_0 = const()[name = tensor("hidden_states_45_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098496)))]; tensor decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393472)))]; tensor hidden_states_45_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_45_dilations_0, groups = hidden_states_45_groups_0, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = hidden_states_45_strides_0, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("hidden_states_45_cast_fp16")]; tensor var_766_cast_fp16 = add(x = var_724_cast_fp16, y = hidden_states_45_cast_fp16)[name = tensor("op_766_cast_fp16")]; tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_766_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393792)))]; tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394112)))]; tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; tensor input_323_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_323_cast_fp16")]; tensor var_779_begin_0 = const()[name = tensor("op_779_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_779_end_0 = const()[name = tensor("op_779_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_779_end_mask_0 = const()[name = tensor("op_779_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_779_cast_fp16 = slice_by_index(begin = var_779_begin_0, end = var_779_end_0, end_mask = var_779_end_mask_0, x = input_323_cast_fp16)[name = tensor("op_779_cast_fp16")]; tensor var_780_begin_0 = const()[name = tensor("op_780_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_780_end_0 = const()[name = tensor("op_780_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_780_end_mask_0 = const()[name = tensor("op_780_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_780_cast_fp16 = slice_by_index(begin = var_780_begin_0, end = var_780_end_0, end_mask = var_780_end_mask_0, x = input_323_cast_fp16)[name = tensor("op_780_cast_fp16")]; tensor input_325_interleave_0 = const()[name = tensor("input_325_interleave_0"), val = tensor(false)]; tensor input_325_cast_fp16 = concat(axis = var_24, interleave = input_325_interleave_0, values = (var_779_cast_fp16, input_323_cast_fp16, var_780_cast_fp16))[name = tensor("input_325_cast_fp16")]; tensor input_327_pad_0 = const()[name = tensor("input_327_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_327_mode_0 = const()[name = tensor("input_327_mode_0"), val = tensor("constant")]; tensor const_36_to_fp16 = const()[name = tensor("const_36_to_fp16"), val = tensor(0x0p+0)]; tensor input_327_cast_fp16 = pad(constant_val = const_36_to_fp16, mode = input_327_mode_0, pad = input_327_pad_0, x = input_325_cast_fp16)[name = tensor("input_327_cast_fp16")]; tensor input_329_pad_type_0 = const()[name = tensor("input_329_pad_type_0"), val = tensor("valid")]; tensor input_329_strides_0 = const()[name = tensor("input_329_strides_0"), val = tensor([1, 1])]; tensor input_329_pad_0 = const()[name = tensor("input_329_pad_0"), val = tensor([0, 0, 0, 0])]; tensor input_329_dilations_0 = const()[name = tensor("input_329_dilations_0"), val = tensor([1, 1])]; tensor input_329_groups_0 = const()[name = tensor("input_329_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394432)))]; tensor decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689408)))]; tensor input_329_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = input_329_dilations_0, groups = input_329_groups_0, pad = input_329_pad_0, pad_type = input_329_pad_type_0, strides = input_329_strides_0, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_327_cast_fp16)[name = tensor("input_329_cast_fp16")]; tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_329_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689728)))]; tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690048)))]; tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; tensor input_333_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_333_cast_fp16")]; tensor var_797_begin_0 = const()[name = tensor("op_797_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_797_end_0 = const()[name = tensor("op_797_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_797_end_mask_0 = const()[name = tensor("op_797_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_797_cast_fp16 = slice_by_index(begin = var_797_begin_0, end = var_797_end_0, end_mask = var_797_end_mask_0, x = input_333_cast_fp16)[name = tensor("op_797_cast_fp16")]; tensor var_798_begin_0 = const()[name = tensor("op_798_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_798_end_0 = const()[name = tensor("op_798_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_798_end_mask_0 = const()[name = tensor("op_798_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_798_cast_fp16 = slice_by_index(begin = var_798_begin_0, end = var_798_end_0, end_mask = var_798_end_mask_0, x = input_333_cast_fp16)[name = tensor("op_798_cast_fp16")]; tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; tensor input_337_cast_fp16 = concat(axis = var_24, interleave = input_337_interleave_0, values = (var_797_cast_fp16, input_333_cast_fp16, var_798_cast_fp16))[name = tensor("input_337_cast_fp16")]; tensor input_339_pad_0 = const()[name = tensor("input_339_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_339_mode_0 = const()[name = tensor("input_339_mode_0"), val = tensor("constant")]; tensor const_37_to_fp16 = const()[name = tensor("const_37_to_fp16"), val = tensor(0x0p+0)]; tensor input_339_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_339_mode_0, pad = input_339_pad_0, x = input_337_cast_fp16)[name = tensor("input_339_cast_fp16")]; tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("valid")]; tensor hidden_states_strides_0 = const()[name = tensor("hidden_states_strides_0"), val = tensor([1, 1])]; tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; tensor hidden_states_dilations_0 = const()[name = tensor("hidden_states_dilations_0"), val = tensor([1, 1])]; tensor hidden_states_groups_0 = const()[name = tensor("hidden_states_groups_0"), val = tensor(1)]; tensor decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690368)))]; tensor decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985344)))]; tensor hidden_states_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_339_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; tensor var_808_cast_fp16 = add(x = var_766_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("op_808_cast_fp16")]; tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 512, 1024])]; tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_808_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 512, 1024])]; tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985664)))]; tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985984)))]; tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; tensor input_345_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_345_cast_fp16")]; tensor var_817_begin_0 = const()[name = tensor("op_817_begin_0"), val = tensor([0, 0, 0, -1])]; tensor var_817_end_0 = const()[name = tensor("op_817_end_0"), val = tensor([1, 128, 512, 1024])]; tensor var_817_end_mask_0 = const()[name = tensor("op_817_end_mask_0"), val = tensor([true, true, true, true])]; tensor var_817_cast_fp16 = slice_by_index(begin = var_817_begin_0, end = var_817_end_0, end_mask = var_817_end_mask_0, x = input_345_cast_fp16)[name = tensor("op_817_cast_fp16")]; tensor var_818_begin_0 = const()[name = tensor("op_818_begin_0"), val = tensor([0, 0, 0, 0])]; tensor var_818_end_0 = const()[name = tensor("op_818_end_0"), val = tensor([1, 128, 512, 1])]; tensor var_818_end_mask_0 = const()[name = tensor("op_818_end_mask_0"), val = tensor([true, true, true, false])]; tensor var_818_cast_fp16 = slice_by_index(begin = var_818_begin_0, end = var_818_end_0, end_mask = var_818_end_mask_0, x = input_345_cast_fp16)[name = tensor("op_818_cast_fp16")]; tensor input_347_interleave_0 = const()[name = tensor("input_347_interleave_0"), val = tensor(false)]; tensor input_347_cast_fp16 = concat(axis = var_24, interleave = input_347_interleave_0, values = (var_817_cast_fp16, input_345_cast_fp16, var_818_cast_fp16))[name = tensor("input_347_cast_fp16")]; tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([0, 0, 0, 0, 1, 1, 0, 0])]; tensor input_mode_0 = const()[name = tensor("input_mode_0"), val = tensor("constant")]; tensor const_38_to_fp16 = const()[name = tensor("const_38_to_fp16"), val = tensor(0x0p+0)]; tensor input_cast_fp16 = pad(constant_val = const_38_to_fp16, mode = input_mode_0, pad = input_pad_0, x = input_347_cast_fp16)[name = tensor("input_cast_fp16")]; tensor var_827_pad_type_0 = const()[name = tensor("op_827_pad_type_0"), val = tensor("valid")]; tensor var_827_strides_0 = const()[name = tensor("op_827_strides_0"), val = tensor([1, 1])]; tensor var_827_pad_0 = const()[name = tensor("op_827_pad_0"), val = tensor([0, 0, 0, 0])]; tensor var_827_dilations_0 = const()[name = tensor("op_827_dilations_0"), val = tensor([1, 1])]; tensor var_827_groups_0 = const()[name = tensor("op_827_groups_0"), val = tensor(1)]; tensor decoder_conv_out_weight_to_fp16 = const()[name = tensor("decoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98986304)))]; tensor decoder_conv_out_bias_to_fp16 = const()[name = tensor("decoder_conv_out_bias_to_fp16"), val = tensor([0x1.514p-8, -0x1.c4cp-6, -0x1.67p-5])]; tensor var_827_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_827_dilations_0, groups = var_827_groups_0, pad = var_827_pad_0, pad_type = var_827_pad_type_0, strides = var_827_strides_0, weight = decoder_conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_827_cast_fp16")]; tensor var_827_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_827_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; tensor image = cast(dtype = var_827_cast_fp16_to_fp32_dtype_0, x = var_827_cast_fp16)[name = tensor("cast_37")]; } -> (image); }