// the same because all strides are 1).
      low_padding[dim] =
          conv_grad_spatial_dim.pad_before - orig_padding[dim].first;
      high_padding[dim] =
          conv_grad_spatial_dim.pad_after - orig_padding[dim].second;

      // Update `out_grad_shape` to result shape of following `PadOp`.
      out_grad_shape[dim] = low_padding[dim] + high_padding[dim] +

  for (size_t i = 0; i < window_dimensions.size(); i++) {
    auto dim = window.add_dimensions();
    dim->set_size(window_dimensions[i]);
    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 {

    shape.reserve(shape_ref.size());
    for (auto& dim : shape_ref) {
      // translate dynamic shapes from mlir to tfl values
      shape.push_back(
          dim == mlir::ShapedType::kDynamic ? 1 : static_cast<int>(dim));
      shape_signature.push_back(static_cast<int>(
          dim == mlir::ShapedType::kDynamic ? tensorflow::kTFDynamicSize
                                            : dim));
    }
  }

The conversion can happen partially in subgroups, by specifying the dim
attribute, where only that dim will be converted.
  }];

  let arguments = (ins
    TF_Tensor:$input,

    StrAttr:$manual_sharding,
    DefaultValuedOptionalAttr<I64Attr, "-1">:$dim,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$unspecified_dims
  );

  let results = (outs

* `tf.argmax`: `dimension` becomes `axis` * `tf.argmin`: `dimension` becomes
`axis` * `tf.count_nonzero`: `reduction_indices` becomes `axis` *
`tf.expand_dims`: `dim` becomes `axis` * `tf.reduce_all`: `reduction_indices`
becomes `axis` * `tf.reduce_any`: `reduction_indices` becomes `axis` *
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:

							Default:     0,
							Type:        []string{"integer"},
							Format:      "int32",
						},
					},
					"versionPriority": {

          "format": "int32",
          "type": "integer"
        },
        "insecureSkipTLSVerify": {