# Model class API
In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a `Model` via:
“`python
from keras.models import Model
from keras.layers import Input, Dense
a = Input(shape=(32,))
b = Dense(32)(a)
model = Model(inputs=a, outputs=b)
“`
This model will include all layers required in the computation of `b` given `a`.
In the case of multi-input or multi-output models, you can use lists as well:
“`python
model = Model(inputs=[a1, a2], outputs=[b1, b3, b3])
“`
For a detailed introduction of what `Model` can do, read [this guide to the Keras functional API](/getting-started/functional-api-guide).
## Useful attributes of Model
– `model.layers` is a flattened list of the layers comprising the model graph.
– `model.inputs` is the list of input tensors.
– `model.outputs` is the list of output tensors.
## Methods
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