[FRONTEND][TENSORFLOW] bug fix for tensorflow official slim models. (#2864)

* [FRONTEND][TENSORFLOW] bug fix for tensorflow official slim models.

* 	* review comments

python/tvm/relay/frontend/tensorflow.py

@@ -543,25 +543,23 @@ def _reshape():
                 op_name="reshape",
                 extras={'newshape':tuple(shape_arg.asnumpy())},
                 ignores=['Tshape'])(inputs, attr)
-        except KeyError:
+        except AttributeError:
             # Shape operator is already pruned, hence
             # try to infer shape by precompute prune if possible.
-            if all(in_node in params for in_node in inputs[1].list_input_names()):
-                func = _expr.Function(ir_pass.free_vars(inputs[1]), inputs[1])
-                with tvm.relay.build_config(opt_level=0):
-                    graph, lib, params = tvm.relay.build(func, target="llvm", params=params)
-                ctx = tvm.context("llvm", 0)
-                from tvm.contrib import graph_runtime
-                m = graph_runtime.create(graph, lib, ctx)
-                m.set_input(**params)
-                m.run()
-                params_new = m.get_output(0)
-                inputs.pop(1)
-                return AttrCvt(
-                    op_name="reshape",
-                    extras={'newshape':tuple(params_new.asnumpy().flatten())},
-                    ignores=['Tshape'])(inputs, attr)
-            raise RuntimeError("Reshape with dynamic shape input not supported yet.")
+            func = _expr.Function(ir_pass.free_vars(inputs[1]), inputs[1])
+            with tvm.relay.build_config(opt_level=0):
+                graph, lib, params = tvm.relay.build(func, target="llvm", params=params)
+            ctx = tvm.context("llvm", 0)
+            from tvm.contrib import graph_runtime
+            m = graph_runtime.create(graph, lib, ctx)
+            m.set_input(**params)
+            m.run()
+            params_new = m.get_output(0)
+            inputs.pop(1)
+            return AttrCvt(
+                op_name="reshape",
+                extras={'newshape':tuple(params_new.asnumpy().astype('int64').flatten())},
+                ignores=['Tshape'])(inputs, attr)
     return _impl
 
 def _bias_add():