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,

                                                           TFLOp op) {
  TfLitePoolParams pool_params;
  pool_params.stride_height = op.stride_h().getSExtValue();
  pool_params.stride_width = op.stride_w().getSExtValue();
  pool_params.filter_height = op.filter_h().getSExtValue();
  pool_params.filter_width = op.filter_w().getSExtValue();
  const auto padding = GetTflitePadding(inst, op.padding());

      }

      @Override
      public int compare(byte[] left, byte[] right) {
        int stride = 8;
        int minLength = Math.min(left.length, right.length);
        int strideLimit = minLength & ~(stride - 1);
        int i;

        /*
         * Compare 8 bytes at a time. Benchmarking on x86 shows a stride of 8 bytes is no slower

      }

      @Override
      public int compare(byte[] left, byte[] right) {
        int stride = 8;
        int minLength = Math.min(left.length, right.length);
        int strideLimit = minLength & ~(stride - 1);
        int i;

        /*
         * Compare 8 bytes at a time. Benchmarking on x86 shows a stride of 8 bytes is no slower

In detail, with the default NHWC format,

    output[b, i, j, k] =
        sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                        filter[di, dj, q, k]

Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
  }];

  let arguments = (ins

      llvm::errs() << "Window " << window.DebugString()
                   << " has a non-positive dimension.\n";
      return std::nullopt;
    }
    if (dim.stride() <= 0) {
      llvm::errs() << "Window " << window.DebugString()
                   << " has a non-positive stride.\n";
      return std::nullopt;
    }
    if (dim.base_dilation() < 1) {
      llvm::errs() << "Window " << window.DebugString()

        return ...
      }
      func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) {
        %6 = "tf.Conv2D"(%input, %filter)  {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

    The program will be transformed into:

    ```mlir
    module {

	//     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,

*   Adds FULLY_CONNECTED Op to tensorflow/lite/schema.fbs

## Known Issues

*   Tensorflow_gpu compilation fails with Bazel 0.5.3.

## Bug Fixes and Other Changes

*   Fixes `strides` and `begin` dtype mismatch when slicing using int64 Tensor
    index in python.
*   Improved convolution padding documentation.
*   Add a tag constant, gpu, to present graph with GPU support.