Python: Exclude certain fields from KernelFunction model dump (#11081)

### Motivation and Context

When trying to do a model dump for `KernelFunction` models, they are
failing with a message related to:

```
pydantic_core._pydantic_core.PydanticSerializationError: Unable to serialize unknown type: <class 'type'>
```

There are some class-level attributes that cannot be properly
serialized.

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### Description

Exclude dumping certain model attributes from the model dump, which
allows the main KernelFunction metadata and its underlying parameters to
be serialized.
- Closes #11078 
- Adds unit tests to ensure we have coverage and can dump Pydantic
models.

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄

Author: moonbox3

python/semantic_kernel/functions/kernel_function.py

@@ -92,9 +92,11 @@ class KernelFunction(KernelBaseModel):
 
     metadata: KernelFunctionMetadata
 
-    invocation_duration_histogram: metrics.Histogram = Field(default_factory=_create_function_duration_histogram)
+    invocation_duration_histogram: metrics.Histogram = Field(
+        default_factory=_create_function_duration_histogram, exclude=True
+    )
     streaming_duration_histogram: metrics.Histogram = Field(
-        default_factory=_create_function_streaming_duration_histogram
+        default_factory=_create_function_streaming_duration_histogram, exclude=True
     )
 
     @classmethod

python/semantic_kernel/functions/kernel_function_from_method.py

@@ -6,7 +6,7 @@
 from inspect import isasyncgen, isasyncgenfunction, isawaitable, iscoroutinefunction, isgenerator, isgeneratorfunction
 from typing import Any
 
-from pydantic import ValidationError
+from pydantic import Field, ValidationError
 
 from semantic_kernel.exceptions import FunctionExecutionException, FunctionInitializationError
 from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext
@@ -21,8 +21,8 @@
 class KernelFunctionFromMethod(KernelFunction):
     """Semantic Kernel Function from a method."""
 
-    method: Callable[..., Any]
-    stream_method: Callable[..., Any] | None = None
+    method: Callable[..., Any] = Field(exclude=True)
+    stream_method: Callable[..., Any] | None = Field(default=None, exclude=True)
 
     def __init__(
         self,

python/semantic_kernel/functions/kernel_parameter_metadata.py

@@ -17,7 +17,7 @@ class KernelParameterMetadata(KernelBaseModel):
     default_value: Any | None = None
     type_: str | None = Field(default="str", alias="type")
     is_required: bool | None = False
-    type_object: Any | None = None
+    type_object: Any | None = Field(default=None, exclude=True)
     schema_data: dict[str, Any] | None = None
     include_in_function_choices: bool = True
 

python/tests/unit/functions/test_kernel_function_from_method.py

@@ -544,3 +544,25 @@ def test_gather_function_parameters_exception_handling(get_custom_type_function_
 
     with pytest.raises(FunctionExecutionException, match=r"Parameter param is expected to be parsed to .* but is not."):
         func.gather_function_parameters(context)
+
+
+@pytest.mark.parametrize(
+    ("mode"),
+    [
+        ("python"),
+        ("json"),
+    ],
+)
+def test_function_model_dump(get_custom_type_function_pydantic, mode):
+    func: KernelFunctionFromMethod = get_custom_type_function_pydantic
+    model_dump = func.model_dump(mode=mode)
+    assert isinstance(model_dump, dict)
+    assert "metadata" in model_dump
+    assert len(model_dump["metadata"]["parameters"]) == 1
+
+
+def test_function_model_dump_json(get_custom_type_function_pydantic):
+    func = get_custom_type_function_pydantic
+    model_dump = func.model_dump_json()
+    assert isinstance(model_dump, str)
+    assert "metadata" in model_dump

python/tests/unit/functions/test_kernel_function_from_prompt.py

@@ -382,3 +382,39 @@ async def prompt_rendering_filter(context: PromptRenderContext, next):
     context = FunctionInvocationContext(function=function, kernel=kernel, arguments=KernelArguments())
     prompt_render_result = await function._render_prompt(context)
     assert prompt_render_result.rendered_prompt == "preface test"
+
+
+@pytest.mark.parametrize(
+    ("mode"),
+    [
+        ("python"),
+        ("json"),
+    ],
+)
+def test_function_model_dump(mode: str):
+    function = KernelFunctionFromPrompt(
+        function_name="test",
+        plugin_name="test",
+        prompt="test",
+        template_format="semantic-kernel",
+        prompt_template_config=PromptTemplateConfig(
+            template="test",
+            input_variables=[InputVariable(name="input", type="str", default="test", is_required=False)],
+        ),
+    )
+    model_dump = function.model_dump(mode=mode)
+    assert isinstance(model_dump, dict)
+    assert "metadata" in model_dump
+    assert len(model_dump["metadata"]["parameters"]) == 1
+
+
+def test_function_model_dump_json():
+    function = KernelFunctionFromPrompt(
+        function_name="test",
+        plugin_name="test",
+        prompt="test",
+        template_format="semantic-kernel",
+    )
+    model_dump_json = function.model_dump_json()
+    assert isinstance(model_dump_json, str)
+    assert "test" in model_dump_json