namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//

// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * An invalid shape is being set (e.g., the shape being set
//     is incompatible with the existing shape).
TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.

        populatedArray.count = 3;
        populatedArray.s = new netdfs.DfsInfo200[3];

        // Create DFS root entries
        String[] rootNames = { "share1", "share2", "share3" };
        for (int i = 0; i < 3; i++) {
            netdfs.DfsInfo200 entry = new netdfs.DfsInfo200();
            entry.dfs_name = rootNames[i];
            populatedArray.s[i] = entry;
        }

*   Tracing/timeline support for distributed runtime (no GPU profiler yet).
*   C API gives access to inferred shapes with `TF_GraphGetTensorNumDims` and
    `TF_GraphGetTensorShape`.
*   Shape functions for core ops have moved to C++ via
    `REGISTER_OP(...).SetShapeFn(...)`. Python shape inference RegisterShape
    calls use the C++ shape functions with `common_shapes.call_cpp_shape_fn`. A
    future release will remove `RegisterShape` from python.

        SmbShareInfo share2 = (SmbShareInfo) response.results[1];
        assertEquals("IPC$", share2.getName().trim(), "Share 2 name should be correct.");
        assertEquals(3, share2.type, "Share 2 raw type should be 3.");
        assertEquals("Inter-Process Communication", share2.remark.trim(), "Share 2 remark should be correct.");
    }

    /**

    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));
    }
  }

  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);

  // Infer shape when everything is known.
  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), make_shape({2, 4})},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, 4}));

  // Infer shape when second operand has unknown shape.
  CheckOutputShapes(matmul_op,

        dfsEnumArray.s[0] = new DfsInfo200();
        dfsEnumArray.s[0].dfs_name = "\\domain\share1";
        dfsEnumArray.s[1] = new DfsInfo200();
        dfsEnumArray.s[1].dfs_name = "\\domain\share2";

        // Manually set the DfsEnumArray200 to the info struct
        dfsRootEnum.info.e = dfsEnumArray;

        // When
        FileEntry[] entries = dfsRootEnum.getEntries();

        // Then

    void testEquals() {
        SmbShareInfo instance1 = new SmbShareInfo("SHARE1", 0, "remark1");
        SmbShareInfo instance2 = new SmbShareInfo("SHARE1", 1, "remark2");
        SmbShareInfo instance3 = new SmbShareInfo("SHARE2", 0, "remark1");
        Object notAShareInfo = new Object();

        assertTrue(instance1.equals(instance2)); // Should be equal based on netName
        assertFalse(instance1.equals(instance3)); // Should not be equal