[RedSuns Review] Change tpc API in keras tutorials.

tutorials/notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb

@@ -360,7 +360,7 @@
     "from model_compression_toolkit.core import QuantizationErrorMethod\n",
     "\n",
     "# Specify the IMX500-v1 target platform capability (TPC) \n",
-    "tpc = mct.get_target_platform_capabilities(\"tensorflow\", 'imx500', target_platform_version='v1')\n",
+    "tpc = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')\n",
     "\n",
     "# Set the following quantization configurations:\n",
     "# Choose the desired QuantizationErrorMethod for the quantization parameters search.\n",

tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_threshold_search.ipynb

@@ -287,7 +287,7 @@
    },
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the TPC object for imx500 hardware with version 1.0:"
    ]
   },
   {
@@ -301,8 +301,8 @@
    "source": [
     "import model_compression_toolkit as mct\n",
     "\n",
-    "# Get a FrameworkQuantizationCapabilities object that models the hardware for the quantized model inference. Here, for example, we use the default platform that is attached to a Keras layers representation.\n",
-    "target_platform_cap = mct.get_target_platform_capabilities('tensorflow', 'default')"
+    "# Get a TargetPlatformCapabilities object that models the hardware platform for the quantized model inference. Here, for example, we use the TPC object for imx500 hardware with version 1.0.\n",
+    "target_platform_cap = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')"
    ]
   },
   {

tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_z_score_threshold.ipynb

@@ -271,7 +271,7 @@
    },
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the TPC object for imx500 hardware with version 1.0:"
    ]
   },
   {
@@ -285,8 +285,8 @@
    "source": [
     "import model_compression_toolkit as mct\n",
     "\n",
-    "# Get a FrameworkQuantizationCapabilities object that models the hardware for the quantized model inference. Here, for example, we use the default platform that is attached to a Keras layers representation.\n",
-    "target_platform_cap = mct.get_target_platform_capabilities('tensorflow', 'default')"
+    "# Get a TargetPlatformCapabilities object that models the hardware platform for the quantized model inference. Here, for example, we use the TPC object for imx500 hardware with version 1.0.\n",
+    "target_platform_cap = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')"
    ]
   },
   {

tutorials/notebooks/mct_features_notebooks/keras/example_keras_mobilenet_gptq.ipynb

@@ -285,7 +285,7 @@
     "gptq_config = mct.gptq.get_keras_gptq_config(n_epochs=50)\n",
     "\n",
     "# Specify the target platform capability (TPC)\n",
-    "tpc = mct.get_target_platform_capabilities(\"tensorflow\", 'imx500', target_platform_version='v1')\n"
+    "tpc = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')\n"
    ]
   },
   {

tutorials/notebooks/mct_features_notebooks/keras/example_keras_mobilenet_mixed_precision.ipynb

@@ -254,7 +254,8 @@
    "source": [
     "## Target Platform Capabilities (TPC)\n",
     "In addition, MCT optimizes models for dedicated hardware platforms using Target Platform Capabilities (TPC). \n",
-    "**Note:** To apply mixed-precision quantization to specific layers, the TPC must define different bit-width options for those layers. For more details, please refer to our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html). In this example, we use the default Tensorflow TPC, which supports 2, 4, and 8-bit options for convolution and linear layers"
+    "\n",
+    "**Note:** To apply mixed-precision quantization to specific layers, the TPC must define different bit-width options for those layers. For more details, please refer to our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html). In this example, we use the TPC object for imx500 hardware with version 1.0, which supports 2, 4, and 8-bit options for convolution and linear layers"
    ]
   },
   {
@@ -266,8 +267,8 @@
    },
    "outputs": [],
    "source": [
-    "# Get a FrameworkQuantizationCapabilities object that models the hardware platform for the quantized model inference. Here, for example, we use the default platform that is attached to a Keras layers' representation.\n",
-    "target_platform_cap  = mct.get_target_platform_capabilities(\"tensorflow\", 'imx500', target_platform_version='v1')"
+    "# Get a TargetPlatformCapabilities object that models the hardware platform for the quantized model inference. Here, for example, we use the TPC object for imx500 hardware with version 1.0.\n",
+    "target_platform_cap = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')"
    ]
   },
   {

tutorials/notebooks/mct_features_notebooks/keras/example_keras_post-training_quantization.ipynb

@@ -248,7 +248,7 @@
    },
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the TPC object for imx500 hardware with version 1.0:"
    ]
   },
   {
@@ -262,8 +262,8 @@
    "source": [
     "import model_compression_toolkit as mct\n",
     "\n",
-    "# Get a FrameworkQuantizationCapabilities object that models the hardware for the quantized model inference. Here, for example, we use the default platform that is attached to a Keras layers representation.\n",
-    "target_platform_cap = mct.get_target_platform_capabilities('tensorflow', 'default')"
+    "# Get a TargetPlatformCapabilities object that models the hardware platform for the quantized model inference. Here, for example, we use the TPC object for imx500 hardware with version 1.0.\n",
+    "target_platform_cap = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')"
    ]
   },
   {

tutorials/notebooks/task_notebooks/keras/example_effdet_keras_mixed_precision_ptq.ipynb

@@ -555,7 +555,7 @@
    "metadata": {},
    "source": [
     "## Target Platform Capabilities (TPC)\n",
-    "In addition, MCT optimizes the model for dedicated hardware platforms. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Keras TPC:"
+    "In addition, MCT optimizes the model for dedicated hardware platforms. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the TPC object for imx500 hardware with version 1.0:"
    ]
   },
   {
@@ -565,7 +565,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tpc = mct.get_target_platform_capabilities('tensorflow', 'default')"
+    "tpc = mct.get_target_platform_capabilities(tpc_version='1.0', device_type='imx500')"
    ]
   },
   {