class_docstr: Converts raw waveforms to batches of examples to use as inputs to VGGish.
docstr: Args:
      input (torch.Tensor): waveform, with shape `(T,)`.
      sample_rate (int): sample rate of waveform in hertz.
  
  Returns:
      torch.Tensor: batch of examples to pass to VGGish, with shape `(n_example, 1, n_frame, 64)`.
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code: def __call__(self, input: torch.Tensor) -> torch.Tensor:
      """
      Args:
          input (torch.Tensor): waveform, with shape `(T,)`.
          sample_rate (int): sample rate of waveform in hertz.
  
      Returns:
          torch.Tensor: batch of examples to pass to VGGish, with shape `(n_example, 1, n_frame, 64)`.
      """
      if len(input.shape) != 1:
          raise ValueError("input waveform must have dimension of 1.")
      return _waveform_to_examples(input)
  
file_path: audio/src/torchaudio/prototype/pipelines/_vggish/_vggish_impl.py
imports: import math
  import torch
label_desc: Use image node_id 1 for calling the VGGishInputProcessor obj's underlying member method code with example usage: obj.__call__(input) and returns: _waveform_to_examples