class_docstr: Dialect op for N-D IRFFT (irfftn).
  
  Parameters
  ----------
  See the ``complex_ifftn`` op for details.
  
  Returns
  -------
  tensor<\*V, fp32>
      * The shape is the same as the input except for:
          * If ``shapes`` and ``dims`` are both None, shape at the last dim ``V[-1]``  is
            ``2 * (D[-1] - 1)``.
          * If ``shapes`` or ``dims`` is specified, shape at ``dim`` is ``shapes[dim]``.
  
  Attributes
  ----------
  T: complex64
  
  References
  ----------
  See `torch.fft.irfftn <https://pytorch.org/docs/stable/generated/torch.fft.irfftn.html>`_.
class_docstr_tok: ["Dialect","op","for","N-D","IRFFT","(","irfftn",")",".","Parameters","--","--","--","--","--","See","the","``","complex_ifftn","``","op","for","details",".","Returns","--","--","--","-","tensor","<","\\","*","V",",","fp32",">","*","The","shape","is","the","same","as","the","input","except","for",":","*","If","``","shapes","``","and","``","dims","``","are","both","None",",","shape","at","the","last","dim","``","V","[","-1","]","``","is","``","2","*","(","D","[","-1","]","-","1",")","``",".","*","If","``","shapes","``","or","``","dims","``","is","specified",",","shape","at","``","dim","``","is","``","shapes","[","dim","]","``",".","Attributes","--","--","--","--","--","T",":","complex64","References","--","--","--","--","--","See","`","torch.fft.irfftn","<","https",":","\/\/pytorch.org\/docs\/stable\/generated\/torch.fft.irfftn.html",">","`","_","."]
code: def type_inference(self):
      output_type = infer_fp_dtype_from_complex(self.data.dtype)
      output_shape = list(self.data.shape)
      shapes, dims = fft_canonicalize_shapes_dims(
          self.data, self.shapes, self.dims, c2r=True
      )
      for shape, dim in zip(shapes, dims):
          output_shape[dim] = shape
      return types.tensor(output_type, tuple(output_shape))
  
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 2 for calling the complex_irfftn obj's underlying member method code with example usage: obj.type_inference() and returns: types