[MO][TF FE] Handle constant with undefined value (#17311) (#24382)

**Details:** Since TF 2.10 the native model freezing can produce
constants with undefined value, i.e. tensor shape can be any and value
is []. In this case the tensor just fills up with the default value (0 -
for numerics, "" - for strings)

**Ticket:** 140458

Porting of https://github.com/openvinotoolkit/openvino/pull/17311

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

tools/mo/openvino/tools/mo/front/tf/extractors/utils.py

@@ -82,7 +82,7 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor):
             value_length = len(value)
         except TypeError:
             # case, when value is a scalar
-            value_length = 0
+            return value
         if value_length == 1:
             # return scalar if shape is [] otherwise broadcast according to shape
             try:
@@ -91,18 +91,33 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor):
                 log.error(decode_err_msg, extra={'is_warning': True})
                 return mo_array(value[0])
         else:
+            if len(shape) == 0 and value_length == 0:
+                # Since TF 2.10 the model freezing can produce constants with non-empty tensor
+                # but with undefined value []
+                # in this case, the tensor is filled with the default value
+                # that is 0 for numeric types and "" for string
+                default_value = 0 if type_helper[0] != str else ""
+                value = mo_array(default_value, dtype=type_helper[0])
             # no shape, return value as is
             return value
 
     if len(value) != shape.prod():
         log.warning("Shape and content size of tensor don't match, shape: {} content size: {}".
                     format(shape, len(value)))
+
+        if len(value) == 0:
+            # Since TF 2.10 the model freezing can produce constants with non-empty tensor but with undefined value []
+            # In this case, the tensor is filled with the default value that is 0 for numeric types and "" for string
+            default_value = 0 if type_helper[0] != str else ""
+            value_flatten = mo_array([default_value], dtype=type_helper[0])
+        else:
+            value_flatten = value.flatten()
+
         # broadcast semantics according to TensorFlow v1.5 documentation:
         # The argument value can be a constant value, or a list of values of type dtype. If value is a list,
         # then the length of the list must be less than or equal to the number of elements implied by the shape
         # argument (if specified). In the case where the list length is less than the number of elements specified
         # by shape, the last element in the list will be used to fill the remaining entries.
-        value_flatten = value.flatten()
         add_value = value_flatten[-1]
         add_length = shape.prod() - len(value_flatten)
         value = np.concatenate([value_flatten, np.full([add_length], add_value)])

tools/mo/unit_tests/moc_tf_fe/test_models/model_add_with_undefined_constant.pbtxt

@@ -0,0 +1,58 @@
+node {
+  name: "x"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 2
+        }
+        dim {
+          size: 3
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 3
+          }
+        }
+      }
+    }
+  }
+}
+node {
+  name: "add"
+  op: "AddV2"
+  input: "x"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}

tools/mo/unit_tests/moc_tf_fe/test_models/model_add_with_undefined_constant.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import tensorflow.compat.v1 as tf
+
+tf.reset_default_graph()
+# Create the graph and model
+with tf.Session() as sess:
+    x = tf.placeholder(tf.float32, [2, 3], 'x')
+    const = tf.constant(value=[], dtype=tf.float32, shape=[3], name='Const')
+    tf.add(x, const, name="add")
+    tf.global_variables_initializer()
+    tf.io.write_graph(sess.graph, './', 'model_add_with_undefined_constant.pbtxt', as_text=True)

tools/mo/unit_tests/moc_tf_fe/test_models/model_mul_with_undefined_constant.pbtxt

@@ -0,0 +1,52 @@
+node {
+  name: "x"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 2
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_INT32
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+        }
+      }
+    }
+  }
+}
+node {
+  name: "mul"
+  op: "Mul"
+  input: "x"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_INT32
+    }
+  }
+}

tools/mo/unit_tests/moc_tf_fe/test_models/model_mul_with_undefined_constant.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import tensorflow.compat.v1 as tf
+
+tf.reset_default_graph()
+# Create the graph and model
+with tf.Session() as sess:
+    x = tf.placeholder(tf.int32, [2], 'x')
+    const = tf.constant(value=[], dtype=tf.int32, shape=[], name='Const')
+    tf.multiply(x, const, name="mul")
+    tf.global_variables_initializer()
+    tf.io.write_graph(sess.graph, './', 'model_mul_with_undefined_constant.pbtxt', as_text=True)