code: def get_test_data():
      test_data = [
          ({}, {}, {}),
          ({}, {}, {"cache_generator_outputs": True}),
          ({}, {}, {"plot_pred_mos": True}),
          (
              {},
              {
                  "discriminator_type": "hifigan_multi_scale_discriminator",
                  "discriminator_params": {
                      "scales": 2,
                      "downsample_pooling": "AvgPool1d",
                      "downsample_pooling_params": {
                          "kernel_size": 4,
                          "stride": 2,
                          "padding": 2,
                      },
                      "discriminator_params": {
                          "in_channels": 1,
                          "out_channels": 1,
                          "kernel_sizes": [15, 41, 5, 3],
                          "channels": 16,
                          "max_downsample_channels": 32,
                          "max_groups": 16,
                          "bias": True,
                          "downsample_scales": [2, 2, 1],
                          "nonlinear_activation": "LeakyReLU",
                          "nonlinear_activation_params": {
                              "negative_slope": 0.1
                          },
                      },
                  },
              },
              {},
          ),
          (
              {},
              {
                  "discriminator_type": "hifigan_multi_period_discriminator",
                  "discriminator_params": {
                      "periods": [2, 3],
                      "discriminator_params": {
                          "in_channels": 1,
                          "out_channels": 1,
                          "kernel_sizes": [5, 3],
                          "channels": 16,
                          "downsample_scales": [3, 3, 1],
                          "max_downsample_channels": 32,
                          "bias": True,
                          "nonlinear_activation": "LeakyReLU",
                          "nonlinear_activation_params": {
                              "negative_slope": 0.1
                          },
                          "use_weight_norm": True,
                          "use_spectral_norm": False,
                      },
                  },
              },
              {},
          ),
          (
              {},
              {
                  "discriminator_type": "hifigan_period_discriminator",
                  "discriminator_params": {
                      "period": 2,
                      "in_channels": 1,
                      "out_channels": 1,
                      "kernel_sizes": [5, 3],
                      "channels": 16,
                      "downsample_scales": [3, 3, 1],
                      "max_downsample_channels": 32,
                      "bias": True,
                      "nonlinear_activation": "LeakyReLU",
                      "nonlinear_activation_params": {
                          "negative_slope": 0.1
                      },
                      "use_weight_norm": True,
                      "use_spectral_norm": False,
                  },
              },
              {},
          ),
          (
              {},
              {
                  "discriminator_type": "hifigan_scale_discriminator",
                  "discriminator_params": {
                      "in_channels": 1,
                      "out_channels": 1,
                      "kernel_sizes": [15, 41, 5, 3],
                      "channels": 16,
                      "max_downsample_channels": 32,
                      "max_groups": 16,
                      "bias": True,
                      "downsample_scales": [2, 2, 1],
                      "nonlinear_activation": "LeakyReLU",
                      "nonlinear_activation_params": {
                          "negative_slope": 0.1
                      },
                  },
              },
              {},
          ),
          (
              {},
              {},
              {
                  "generator_adv_loss_params": {
                      "average_by_discriminators": True,
                      "loss_type": "mse",
                  },
                  "discriminator_adv_loss_params": {
                      "average_by_discriminators": True,
                      "loss_type": "mse",
                  },
              },
          ),
          (
              {},
              {},
              {
                  "generator_adv_loss_params": {
                      "average_by_discriminators": False,
                      "loss_type": "hinge",
                  },
                  "discriminator_adv_loss_params": {
                      "average_by_discriminators": False,
                      "loss_type": "hinge",
                  },
              },
          ),
      ]
      return test_data
  
file_path: espnet/test/espnet2/gan_tts/vits/test_vits.py
imports: import pytest
  import torch
  from espnet2.gan_tts.vits import VITS
label_desc: Use image node_id 1 for calling a global function with example usage: get_test_data() and returns: test_data