class_docstr: Dialect op for 1-D STFT.
  
  Parameters
  ----------
  input: tensor<\*D, T> (Required)
      * The input tensor.
  n_fft: const i32 (Required)
      * Size of the fourier transform.
  hop_length: const i32 (Optional)
      * Stride between window frames of the input tensor.
  win_length: const i32 (optional)
      * The size of the window frame.
  window: tensor<1, win_length> (optional)
      * The window to apply to the input signal before performing the fourier transform.
  normalized: const bool (optional, Default=``false``)
      * Whether to normalize the results of the STFT
  onesided: const bool (optional, Default=``true``)
      * For real-valued inputs, whether to return the first half of the results.
  
  Returns
  -------
  tensor<\*V, complex64>
      * A complex tensor where real and imag parts have the same shape.
  
  Attributes
  ----------
  T: fp32, complex64
  
  References
  ----------
  See `torch.stft <https://pytorch.org/docs/stable/generated/torch.stft.html>`_.
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code: def default_inputs(self):
      return DefaultInputs(
          hop_length=None,
          win_length=None,
          window=None,
          normalized=False,
          onesided=True,
      )
  
imports: from typing import Optional, Tuple
  import numpy
  from coremltools.converters.mil.mil import Operation, types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, is_symbolic
  from coremltools.converters.mil.mil.types.type_mapping import infer_complex_dtype, infer_fp_dtype_from_complex
  from coremltools.converters.mil.mil.var import ComplexVar, Var
label_desc: Use image node_id 1 for calling the complex_stft obj's underlying member method code with example usage: obj.default_inputs() and returns: DefaultInputs