for padding `input`.

    The padded size of each dimension D of the output is:

      `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`

    For example:

    ```
    # 't' is [[1, 1], [2, 2]]
    # 'paddings' is [[1, 1], [2, 2]]
    # rank of 't' is 2
    pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                          [0, 0, 1, 1, 0, 0]

    return failure();

  ElementsAttr padding;
  if (matchPattern(op.getPadding(), m_Constant(&padding))) {
    const ShapedType &padding_ty = cast<ShapedType>(padding.getType());
    if (padding_ty.getRank() != 2 || padding_ty.getDimSize(1) != 2) {
      return op.emitOpError()
             << "expects padding to be a matrix with minor dimension 2, got "
             << padding.getShapedType().getShape();
    }
  }

    SmallVector<int64_t> padded_shape(tensor_shape.begin(), tensor_shape.end());
    for (int i = 0; i < padded_shape.size(); ++i) {
      // Left padding + right padding.
      int32_t padding = tfl_pad_values[i * 2] + tfl_pad_values[i * 2 + 1];
      padded_shape[i] += padding;
    }

    return padded_shape;
  }

  std::pair<int64_t, int64_t> GetDimSize(

      // stablehlo.convolution op needs explicit padding to be set to model any
      // Transposed-Convolution in JAX/PT. Checking to see if-
      // 1. Pre set padding matches to the desired padding
      // 2. Output size respects the `VALID` padding scenario
      if ((padding[2 * i] == padding[2 * i + 1]) &&
          (((kernel_size - 1) != padding[2 * i]) ||

    return emitOptionalError(op.getLoc(), "Invalid data format provided");
  }

  const StringRef paddings = op.getPadding();
  tensorflow::Padding padding;
  auto padding_is_valid = GetPaddingFromString(paddings.str(), &padding);
  if (!padding_is_valid.ok()) {
    return emitOptionalError(op.getLoc(), "Invalid padding format provided");
  }

  // Verifies that,
  // * Ranks of operands and result are valid

  // CHECK: "VALID"

    if (!window_strides.empty()) {
      dim->set_stride(window_strides[i]);
    } else {
      dim->set_stride(1);
    }
    if (!padding.empty()) {
      dim->set_padding_low(padding[i].first);
      dim->set_padding_high(padding[i].second);
    } else {
      dim->set_padding_low(0);
      dim->set_padding_high(0);
    }
    if (!lhs_dilation.empty()) {

    %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
    %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
    // expected-error @below {{'tf.XlaConvV2' op feature_group_count must be a positive number, got -1}}

  // OK
  %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  // Unsupported ksize
  %1 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [3, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  // Unsupported strides

// CHECK: stablehlo.convolution
// CHECK-NOT: tfl.conv_2d

// -----

// Tests that if the window padding contains values of 0, tfl.pad op is not
// created and the `padding` attribute is set as "VALID".