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- MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
- signature_def['__saved_model_init_op']:
- The given SavedModel SignatureDef contains the following input(s):
- The given SavedModel SignatureDef contains the following output(s):
- outputs['__saved_model_init_op'] tensor_info:
- dtype: DT_INVALID
- shape: unknown_rank
- name: NoOp
- Method name is:
- signature_def['serving_default']:
- The given SavedModel SignatureDef contains the following input(s):
- inputs['input'] tensor_info:
- dtype: DT_FLOAT
- shape: (1, 7, 96, 2, 2)
- name: serving_default_input:0
- The given SavedModel SignatureDef contains the following output(s):
- outputs['output_0'] tensor_info:
- dtype: DT_FLOAT
- shape: (1, 96, 2)
- name: StatefulPartitionedCall:0
- outputs['output_1'] tensor_info:
- dtype: DT_FLOAT
- shape: (1, 96, 2)
- name: StatefulPartitionedCall:1
- outputs['output_2'] tensor_info:
- dtype: DT_FLOAT
- shape: (1, 96, 2)
- name: StatefulPartitionedCall:2
- outputs['output_3'] tensor_info:
- dtype: DT_FLOAT
- shape: (1, 96, 2)
- name: StatefulPartitionedCall:3
- Method name is: tensorflow/serving/predict
- Defined Functions:
- Function Name: '__call__'
- Option #1
- Callable with:
- Argument #1
- input: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='input')
- Argument #2
- DType: bool
- Value: True
- Argument #3
- DType: NoneType
- Value: None
- Option #2
- Callable with:
- Argument #1
- inputs: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='inputs')
- Argument #2
- DType: bool
- Value: True
- Argument #3
- DType: NoneType
- Value: None
- Option #3
- Callable with:
- Argument #1
- inputs: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='inputs')
- Argument #2
- DType: bool
- Value: False
- Argument #3
- DType: NoneType
- Value: None
- Option #4
- Callable with:
- Argument #1
- input: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='input')
- Argument #2
- DType: bool
- Value: False
- Argument #3
- DType: NoneType
- Value: None
- Function Name: '_default_save_signature'
- Option #1
- Callable with:
- Argument #1
- input: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='input')
- Function Name: 'call_and_return_all_conditional_losses'
- Option #1
- Callable with:
- Argument #1
- inputs: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='inputs')
- Argument #2
- DType: bool
- Value: True
- Argument #3
- DType: NoneType
- Value: None
- Option #2
- Callable with:
- Argument #1
- inputs: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='inputs')
- Argument #2
- DType: bool
- Value: False
- Argument #3
- DType: NoneType
- Value: None
- Option #3
- Callable with:
- Argument #1
- input: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='input')
- Argument #2
- DType: bool
- Value: True
- Argument #3
- DType: NoneType
- Value: None
- Option #4
- Callable with:
- Argument #1
- input: TensorSpec(shape=(None, 7, 96, 2, 2), dtype=tf.float32, name='input')
- Argument #2
- DType: bool
- Value: False
- Argument #3
- DType: NoneType
- Value: None
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