code: def sample_tts_input(
      export_config,
      device,
      max_batch=1,
      max_dim=127,
  ):
      """
      Generates input examples for tracing etc.
      Returns:
          A tuple of input examples.
      """
      sz = (
          (max_batch * max_dim,)
          if export_config["enable_ragged_batches"]
          else (max_batch, max_dim)
      )
      inp = torch.randint(
          *export_config["emb_range"],
          sz,
          device=device,
          dtype=torch.int64,
      )
      pitch = torch.randn(sz, device=device, dtype=torch.float32) * 0.5
      pace = torch.clamp(
          torch.randn(sz, device=device, dtype=torch.float32) * 0.1
          + 1.0,
          min=0.2,
      )
      inputs = {"text": inp, "pitch": pitch, "pace": pace}
      if export_config["enable_ragged_batches"]:
          batch_lengths = torch.zeros(
              (max_batch + 1), device=device, dtype=torch.int32
          )
          left_over_size = sz[0]
          batch_lengths[0] = 0
          for i in range(1, max_batch):
              equal_len = (left_over_size - (max_batch - i)) // (
                  max_batch - i
              )
              length = torch.randint(
                  equal_len // 2,
                  equal_len,
                  (1,),
                  device=device,
                  dtype=torch.int32,
              )
              batch_lengths[i] = length + batch_lengths[i - 1]
              left_over_size -= length.detach().cpu().numpy()[0]
          batch_lengths[-1] = left_over_size + batch_lengths[-2]
  
          sum = 0
          index = 1
          while index < len(batch_lengths):
              sum += batch_lengths[index] - batch_lengths[index - 1]
              index += 1
          assert (
              sum == sz[0]
          ), f"sum: {sum}, sz: {sz[0]}, lengths:{batch_lengths}"
      else:
          batch_lengths = torch.randint(
              max_dim // 2,
              max_dim,
              (max_batch,),
              device=device,
              dtype=torch.int32,
          )
          batch_lengths[0] = max_dim
      inputs["batch_lengths"] = batch_lengths
  
      if export_config["enable_volume"]:
          volume = torch.clamp(
              torch.randn(sz, device=device, dtype=torch.float32) * 0.1
              + 1,
              min=0.01,
          )
          inputs["volume"] = volume
  
      if "num_speakers" in export_config:
          inputs["speaker"] = torch.randint(
              0,
              export_config["num_speakers"],
              (max_batch,),
              device=device,
              dtype=torch.int64,
          )
      return inputs
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import librosa
  import matplotlib.pylab
  import numpy
  import torch
  from einops import rearrange
  from numba import jit, prange
  from nemo.collections.tts.torch.tts_data_types import DATA_STR2DATA_CLASS, MAIN_DATA_TYPES, WithLens
  from nemo.utils import logging
  from nemo.utils.decorators import deprecated
label_desc: Use image node_id 35 for calling a global function with example usage: sample_tts_input(export_config, device, max_batch, max_dim) and returns: inputs