let description = [{
Computes the 2-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 2 dimensions of `input`.

Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
`fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
of `output`: the zero-frequency term, followed by the `fft_length / 2`
positive-frequency terms.

  %paddings = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>
  // expected-error @+1 {{failed to verify that operand 1 is 2-D}}
  %0 = "tf.MirrorPad"(%input, %paddings) { mode = "SYMMETRIC" }: (tensor<2xi64>, tensor<2xi64>) -> tensor<3xi64>
  func.return %0 : tensor<3xi64>
}

// -----

	Data []byte
}

const (
	// DefaultHardPodAffinitySymmetricWeight is the weight of implicit PreferredDuringScheduling affinity rule.
	//
	// RequiredDuringScheduling affinity is not symmetric, but there is an implicit PreferredDuringScheduling affinity rule
	// corresponding to every RequiredDuringScheduling affinity rule.
	// When the --hard-pod-affinity-weight scheduler flag is not specified,

To reduce complexity, type inference ignores the directionality of assignments,
so the type relationship between <code>Slice</code> and <code>S</code> can be
expressed via the (symmetric) type equation <code>Slice ≡<sub>A</sub> S</code>
(or <code>S ≡<sub>A</sub> Slice</code> for that matter),
where the <code><sub>A</sub></code> in <code>≡<sub>A</sub></code>

}

const (
	// DefaultSchedulerName defines the name of default scheduler.
	DefaultSchedulerName = "default-scheduler"

	// RequiredDuringScheduling affinity is not symmetric, but there is an implicit PreferredDuringScheduling affinity rule
	// corresponding to every RequiredDuringScheduling affinity rule.
	// When the --hard-pod-affinity-weight scheduler flag is not specified,

2066..2069    ; disallowed                             # 6.3  LEFT-TO-RIGHT ISOLATE..POP DIRECTIONAL ISOLATE
206A..206F    ; disallowed                             # 1.1  INHIBIT SYMMETRIC SWAPPING..NOMINAL DIGIT SHAPES
2070          ; mapped                 ; 0030          # 1.1  SUPERSCRIPT ZERO
2071          ; mapped                 ; 0069          # 3.2  SUPERSCRIPT LATIN SMALL LETTER I

    MirrorPadMode_SYMMETRIC
  };
  return values;
}

inline const char * const *EnumNamesMirrorPadMode() {
  static const char * const names[3] = {
    "REFLECT",
    "SYMMETRIC",
    nullptr
  };
  return names;
}

inline const char *EnumNameMirrorPadMode(MirrorPadMode e) {
  if (::flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return "";

        `TFLiteConverter.from_saved_model`.
    *   Added DEPTH_TO_SPACE support in Post training quantization.
    *   Added dynamic range quantization support for the BatchMatMul op.
        *   Both symmetric and asymmetric quantized input tensor are supported.
    *   Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently
        only supports float32 input.
    *   Add 5D support to `SLICE` op.