This _should_ fix the issue that is reported in #1595.

It essentially creates a special case for it and applies then the correct way.
This is not very good but it works and I have no better solution.

dace/transformation/dataflow/redundant_array.py

@@ -230,7 +230,7 @@ def can_be_applied(self, graph: SDFGState, expr_index, sdfg, permissive=False):
 
         if not permissive:
             # Make sure the memlet covers the removed array
-            subset = copy.deepcopy(e1.data.subset)
+            subset = copy.deepcopy(a1_subset)
             subset.squeeze()
             shape = [sz for sz in in_desc.shape if sz != 1]
             if any(m != a for m, a in zip(subset.size(), shape)):
@@ -552,6 +552,81 @@ def apply(self, graph, sdfg):
             self._make_view(sdfg, graph, in_array, out_array, e1, b_subset, b_dims_to_pop)
             return in_array
 
+
+        # Special case of a reshaping Memelt, i.e. the Memlet between `in_array` and `out_array`
+        #  performs reshaping. We only handle a special case in which the source has a single
+        #  input. This is a fix for [issue 1595](https://github.com/spcl/dace/issues/1595).
+        #  For a test see 'tests/transformations/redundant_copy_test.py::test_reshaping_with_redundant_arrays'
+        #  as 'Case 1'.
+        #  Furthermore, we require that `in_array` only has a single predecessor, which is an access node.
+        #  This makes our life simpler, because we do not have to rename stuff, however, we should
+        #  lift this restriction later.
+        in_full_read_out_full_written =  all(
+            all(sssize == arraysize for sssize, arraysize in zip(subset.size(), shape))
+            for subset, shape in zip([b_subset, a1_subset], [out_desc.shape, in_desc.shape])
+        )
+        in_out_array_access_nodes = all(isinstance(node, nodes.AccessNode) for node in (in_array, out_array))
+        in_out_both_arrays = all(
+                isinstance(desc, (data.Scalar, data.Array)) and not isinstance(desc, data.View)
+                for desc in (in_desc, out_desc)
+        )
+        is_reshaping_memlet = (
+                in_full_read_out_full_written
+                and in_out_array_access_nodes
+                and in_out_both_arrays
+                and out_desc.shape != in_desc.shape
+                and out_desc.total_size == in_desc.total_size
+                and not e1.data.wcr
+                and not e1.data.wcr_nonatomic
+        )
+
+        # Not part of a reshaping Memlet but our particular implementation.
+        single_access_node_predecessor = (
+                graph.in_degree(in_array) == 1
+                and isinstance(graph.in_edges(in_array)[0].src, nodes.AccessNode)
+        )
+
+        if is_reshaping_memlet and single_access_node_predecessor:
+            in_edge = graph.in_edges(in_array)[0]
+            new_src = in_edge.src
+            new_src_conn = in_edge.src_conn
+            old_src_subset = in_edge.data.src_subset
+            new_dst = out_array
+            new_dst_conn = e1.dst_conn
+            old_dst_subset = b_subset
+
+            if in_edge.data.data == in_array.data:
+                new_data = out_array.data
+                new_subset = old_dst_subset
+                new_other_subset = old_src_subset
+            else:
+                new_data = in_edge.data.data
+                new_subset = old_src_subset
+                new_other_subset = old_dst_subset
+
+            graph.add_edge(
+                    new_src,
+                    new_src_conn,
+                    new_dst,
+                    new_dst_conn,
+                    mm.Memlet(
+                        data=new_data,
+                        subset=new_subset,
+                        other_subset=new_other_subset,
+                        wcr=in_edge.data.wcr,
+                        wcr_nonatomic=in_edge.data.wcr_nonatomic
+                    )
+            )
+
+            # Finally, remove in_array node
+            graph.remove_node(in_array)
+            try:
+                if in_array.data in sdfg.arrays:
+                    sdfg.remove_data(in_array.data)
+            except ValueError:  # Already in use (e.g., with Views)
+                pass
+            return
+
         # 2. Iterate over the e2 edges and traverse the memlet tree
         for e2 in graph.in_edges(in_array):
             path = graph.memlet_tree(e2)