}

    @Override
    public boolean equals(@Nullable Object obj) {
      // In general getClass().isInstance() is bad for equals.
      // But here we fully control the subclasses to ensure symmetry.
      if (getClass().isInstance(obj)) {
        Wrapper that = (Wrapper) obj;
        return wrapped.equals(that.wrapped);
      }
      return false;
    }

    @Override

    const bool symmetric) {
  const float eps = 1e-7;
  ASSERT_THAT(*float_quant_params.min(), SizeIs(1));
  ASSERT_THAT(*float_quant_params.max(), SizeIs(1));
  float float_min = std::min(0.f, float_quant_params.min()->Get(0));
  float float_max = std::max(0.f, float_quant_params.max()->Get(0));
  if (symmetric) {
    // When the symmetric case, ConvertStatsToQDQs in PrepareQuantizePass

			u.tracef("depth %d >= %d", u.depth, unificationDepthLimit)
		}
		if panicAtUnificationDepthLimit {
			panic("unification reached recursion depth limit")
		}
		return false
	}

	// Unification is symmetric, so we can swap the operands.
	// Ensure that if we have at least one
	// - defined type, make sure one is in y
	// - type parameter recorded with u, make sure one is in x

			u.tracef("depth %d >= %d", u.depth, unificationDepthLimit)
		}
		if panicAtUnificationDepthLimit {
			panic("unification reached recursion depth limit")
		}
		return false
	}

	// Unification is symmetric, so we can swap the operands.
	// Ensure that if we have at least one
	// - defined type, make sure one is in y
	// - type parameter recorded with u, make sure one is in x

    count.
*   SinhArcSinh bijector added.
*   Added `Dataset.list_files` API.
*   Introduces new operations and Python bindings for the Cloud TPU.
*   Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
*   Introduces base implementations of ClusterResolvers.
*   Unify memory representations of TensorShape and PartialTensorShape. As a

  }];

  let description = [{
The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
form square matrices.

The input has to be symmetric and positive definite. Only the lower-triangular
part of the input will be used for this operation. The upper-triangular part
will not be read.

The output is a tensor of the same shape as the input