%1 = "tfl.relu"(%0) : (tensor<1x73x73x16xf32>) -> tensor<1x73x73x16xf32>
  func.return %1 : tensor<1x73x73x16xf32>

      return failure();

    // We also need constant begin/end indices and strides to perform padding
    // calculations.
    // Bounded shape after performing strided slice
    SmallVector<int64_t, 4> shape;
    // Bounded begin, end, and strides for strided slice
    SmallVector<int64_t, 4> begin_indices, end_indices, strides;
    if (!op.GetSlicedShapeAndBoundRanges(&shape, &begin_indices, &end_indices,

  // Dynamic stride: when `begin` and `end` inputs are unknown at compile time,
  // `strides` must be known.
  // CHECK: tf.StridedSlice

// CHECK:         }
func.func @convert_slice(%arg0: tensor<1x4672xf32>) -> tensor<1x519xf32> {
  %0 = "mhlo.slice"(%arg0) <{limit_indices = dense<[1, 4672]> : tensor<2xi64>, start_indices = dense<[0, 4153]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}> : (tensor<1x4672xf32>) -> tensor<1x519xf32>
  func.return %0 : tensor<1x519xf32>
}

// CHECK-LABEL:   func @reshape(

	//     rlen = j - i
	//     rptr = ptr + (mask(rcap) & (i * stride))
	//
	// Where mask(x) is 0 if x==0 and -1 if x>0 and stride is the width
	// of the element type.
	stride := s.constInt(types.Types[types.TINT], ptr.Type.Elem().Size())

	// The delta is the number of bytes to offset ptr by.
	delta := s.newValue2(mulOp, types.Types[types.TINT], i, stride)

	// If we're slicing to the point where the capacity is zero,