self, input_tensor: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        k = array_ops.constant(4, dtype=dtypes.int32)
        values, indices = nn_ops.top_k(input_tensor, k, name='TopK')
        adj_values = values + 2
        return {'indices': indices, 'adj_values': adj_values, 'values': values}

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(

      !axis.getSplatValue<llvm::APInt>().isZero()) {
    return {};
  }

  DenseIntElementsAttr indices;
  if (!matchPattern(op.getIndices(), m_Constant(&indices)) ||
      indices.getType().getRank() != 1 || port[1] >= indices.getNumElements()) {
    return {};
  }

  int64_t input_idx = indices.getValues<IntegerAttr>()[port[1]].getInt();
  if (input_idx >= params_ty.getDimSize(0)) return {};

func.func @testGatherToV2(%params: tensor<4x3xf32>, %indices: tensor<1x2xi32>) -> tensor<2x3xf32> {
  // CHECK: %[[AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: "tf.GatherV2"(%arg0, %arg1, %[[AXIS]]) <{batch_dims = 0 : i64}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4x3xf32>, tensor<1x2xi32>, tensor<i32>) -> tensor<2x3xf32>

}

func (i Machine) String() string   { return stringName(uint32(i), machineStrings, false) }
func (i Machine) GoString() string { return stringName(uint32(i), machineStrings, true) }

// Special section indices.
type SectionIndex int

const (
	SHN_UNDEF     SectionIndex = 0      /* Undefined, missing, irrelevant. */
	SHN_LORESERVE SectionIndex = 0xff00 /* First of reserved range. */

        copyAccessEntry(original, newEntry);
        copyWriteEntry(original, newEntry);
        return newEntry;
      }
    };

    // Masks used to compute indices in the following table.

    static final int ACCESS_MASK = 1;
    static final int WRITE_MASK = 2;
    static final int WEAK_MASK = 4;

    /** Look-up table for factories. */

        copyAccessEntry(original, newEntry);
        copyWriteEntry(original, newEntry);
        return newEntry;
      }
    };

    // Masks used to compute indices in the following table.

    static final int ACCESS_MASK = 1;
    static final int WRITE_MASK = 2;
    static final int WEAK_MASK = 4;

    /** Look-up table for factories. */

			s, err := strconv.Unquote(path)
			if err != nil {
				return ""
			}
			path = s
		}
		return path
	})
}

// findVersionElement returns the slice indices of the final version element /vN in path.
// If there is no such element, it returns -1, -1.
func findVersionElement(path string) (i, j int) {
	j = len(path)
	for i = len(path) - 1; i >= 0; i-- {

  %cst = arith.constant dense<[-1, -1, 4]> : tensor<3xi32>
  // expected-error @+1 {{tfl.reshape' op requires 'shape' to have at most one dynamic dimension, but got multiple dynamic dimensions at indices 0 and 1. You need to set up the unspecified size(s) to avoid this problem, for example,setting batch size in keras model or setting unspecified input size(s) with fixed ones.}}