parameters: (  # pylint: disable=invalid-name
      shape=None,
      batch_size=None,
      name=None,
      dtype=None,
      sparse=False,
      tensor=None,
      ragged=False,
      **kwargs)
docstring: `Input()` is used to instantiate a Keras tensor.
  
    A Keras tensor is a tensor object from the underlying backend
    (Theano or TensorFlow), which we augment with certain
    attributes that allow us to build a Keras model
    just by knowing the inputs and outputs of the model.
  
    For instance, if a, b and c are Keras tensors,
    it becomes possible to do:
    `model = Model(input=[a, b], output=c)`
  
    The added Keras attribute is:
        `_keras_history`: Last layer applied to the tensor.
            the entire layer graph is retrievable from that layer,
            recursively.
  
    Arguments:
        shape: A shape tuple (integers), not including the batch size.
            For instance, `shape=(32,)` indicates that the expected input
            will be batches of 32-dimensional vectors. Elements of this tuple
            can be None; 'None' elements represent dimensions where the shape is
            not known.
        batch_size: optional static batch size (integer).
        name: An optional name string for the layer.
            Should be unique in a model (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.
        dtype: The data type expected by the input, as a string
            (`float32`, `float64`, `int32`...)
        sparse: A boolean specifying whether the placeholder to be created is
            sparse. Only one of 'ragged' and 'sparse' can be True.
        tensor: Optional existing tensor to wrap into the `Input` layer.
            If set, the layer will not create a placeholder tensor.
        ragged: A boolean specifying whether the placeholder to be created is
            ragged. Only one of 'ragged' and 'sparse' can be True. In this case,
            values of 'None' in the 'shape' argument represent ragged dimensions.
            For more information about RaggedTensors, see
            https://www.tensorflow.org/guide/ragged_tensors.
        **kwargs: deprecated arguments support. Supports `batch_shape` and
            `batch_input_shape`.
  
    Returns:
      A `tensor`.
  
    Example:
  
    ```python
    # this is a logistic regression in Keras
    x = Input(shape=(32,))
    y = Dense(16, activation='softmax')(x)
    model = Model(x, y)
    ```
  
    Note that even if eager execution is enabled,
    `Input` produces a symbolic tensor (i.e. a placeholder).
    This symbolic tensor can be used with other
    TensorFlow ops, as such:
  
    ```python
    x = Input(shape=(32,))
    y = tf.square(x)
    ```
  
    Raises:
      ValueError: in case of invalid arguments.
docstring_summary: `Input()` is used to instantiate a Keras tensor.
docstring_tokens: Input () is used to instantiate a Keras tensor .
function: def Input(  # pylint: disable=invalid-name
      shape=None,
      batch_size=None,
      name=None,
      dtype=None,
      sparse=False,
      tensor=None,
      ragged=False,
      **kwargs):
    """`Input()` is used to instantiate a Keras tensor.
  
    A Keras tensor is a tensor object from the underlying backend
    (Theano or TensorFlow), which we augment with certain
    attributes that allow us to build a Keras model
    just by knowing the inputs and outputs of the model.
  
    For instance, if a, b and c are Keras tensors,
    it becomes possible to do:
    `model = Model(input=[a, b], output=c)`
  
    The added Keras attribute is:
        `_keras_history`: Last layer applied to the tensor.
            the entire layer graph is retrievable from that layer,
            recursively.
  
    Arguments:
        shape: A shape tuple (integers), not including the batch size.
            For instance, `shape=(32,)` indicates that the expected input
            will be batches of 32-dimensional vectors. Elements of this tuple
            can be None; 'None' elements represent dimensions where the shape is
            not known.
        batch_size: optional static batch size (integer).
        name: An optional name string for the layer.
            Should be unique in a model (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.
        dtype: The data type expected by the input, as a string
            (`float32`, `float64`, `int32`...)
        sparse: A boolean specifying whether the placeholder to be created is
            sparse. Only one of 'ragged' and 'sparse' can be True.
        tensor: Optional existing tensor to wrap into the `Input` layer.
            If set, the layer will not create a placeholder tensor.
        ragged: A boolean specifying whether the placeholder to be created is
            ragged. Only one of 'ragged' and 'sparse' can be True. In this case,
            values of 'None' in the 'shape' argument represent ragged dimensions.
            For more information about RaggedTensors, see
            https://www.tensorflow.org/guide/ragged_tensors.
        **kwargs: deprecated arguments support. Supports `batch_shape` and
            `batch_input_shape`.
  
    Returns:
      A `tensor`.
  
    Example:
  
    ```python
    # this is a logistic regression in Keras
    x = Input(shape=(32,))
    y = Dense(16, activation='softmax')(x)
    model = Model(x, y)
    ```
  
    Note that even if eager execution is enabled,
    `Input` produces a symbolic tensor (i.e. a placeholder).
    This symbolic tensor can be used with other
    TensorFlow ops, as such:
  
    ```python
    x = Input(shape=(32,))
    y = tf.square(x)
    ```
  
    Raises:
      ValueError: in case of invalid arguments.
    """
    if sparse and ragged:
      raise ValueError(
          'Cannot set both sparse and ragged to True in a Keras input.')
  
    input_layer_config = {'name': name, 'dtype': dtype, 'sparse': sparse,
                          'ragged': ragged, 'input_tensor': tensor}
  
    batch_input_shape = kwargs.pop('batch_input_shape',
                                   kwargs.pop('batch_shape', None))
    if shape and batch_input_shape:
      raise ValueError('Only provide the `shape` OR `batch_input_shape` argument '
                       'to Input, not both at the same time.')
    if batch_input_shape:
      shape = batch_input_shape[1:]
      input_layer_config.update({'batch_input_shape': batch_input_shape})
    else:
      input_layer_config.update(
          {'batch_size': batch_size, 'input_shape': shape})
  
    if kwargs:
      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
  
    if shape is None and tensor is None:
      raise ValueError('Please provide to Input either a `shape`'
                       ' or a `tensor` argument. Note that '
                       '`shape` does not include the batch '
                       'dimension.')
  
    input_layer = InputLayer(**input_layer_config)
  
    # Return tensor including `_keras_history`.
    # Note that in this case train_output and test_output are the same pointer.
    outputs = input_layer._inbound_nodes[0].output_tensors
    if len(outputs) == 1:
      return outputs[0]
    else:
      return outputs
function_tokens: def Input ( # pylint: disable=invalid-name shape = None , batch_size = None , name = None , dtype = None , sparse = False , tensor = None , ragged = False , * * kwargs ) : if sparse and ragged : raise ValueError ( 'Cannot set both sparse and ragged to True in a Keras input.' ) input_layer_config = { 'name' : name , 'dtype' : dtype , 'sparse' : sparse , 'ragged' : ragged , 'input_tensor' : tensor } batch_input_shape = kwargs . pop ( 'batch_input_shape' , kwargs . pop ( 'batch_shape' , None ) ) if shape and batch_input_shape : raise ValueError ( 'Only provide the `shape` OR `batch_input_shape` argument ' 'to Input, not both at the same time.' ) if batch_input_shape : shape = batch_input_shape [ 1 : ] input_layer_config . update ( { 'batch_input_shape' : batch_input_shape } ) else : input_layer_config . update ( { 'batch_size' : batch_size , 'input_shape' : shape } ) if kwargs : raise ValueError ( 'Unrecognized keyword arguments:' , kwargs . keys ( ) ) if shape is None and tensor is None : raise ValueError ( 'Please provide to Input either a `shape`' ' or a `tensor` argument. Note that ' '`shape` does not include the batch ' 'dimension.' ) input_layer = InputLayer ( * * input_layer_config ) # Return tensor including `_keras_history`. # Note that in this case train_output and test_output are the same pointer. outputs = input_layer . _inbound_nodes [ 0 ] . output_tensors if len ( outputs ) == 1 : return outputs [ 0 ] else : return outputs