code: def fft_canonicalize_length_dim(
      input_data: Var,
      length: Optional[Var],
      dim: Optional[Var],
      c2r: bool = False,
  ) -> Tuple[int, int]:
      """
      Canonicalize shape and dim for 1-D FFT (based on PyTorch's fft documentation):
      - length: Signal length. If given, the input will either be zero-padded or trimmed to this
        length before computing the FFT.
      - dim: The dimension along which to take the one dimensional FFT.
      - c2r: Use for "complex to real", such as irfft, which takes complex and output real data.
      """
      shapes, dims = fft_canonicalize_shapes_dims(
          input_data, length, dim, c2r
      )
      return shapes[0], dims[0]
  
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 a global function with example usage: fft_canonicalize_length_dim(input_data, length, dim, c2r) and returns: shapes,  dims