Removed Theano refs from comments.

          name=None, dtype=K.floatx(), tensor=None):

  """`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

  (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 and Keras tensors,

  # Arguments

    output_dim: int > 0.

    init: name of initialization function for the weights of the layer

      (see [initializations](../initializations.md)),

      or alternatively, Theano function to use for weights

      initialization. This parameter is only relevant

      (see [initializations](../initializations.md)),.

      This parameter is only relevant

      if you don't pass a `weights` argument.

    activation: name of activation function to use

      (see [activations](../activations.md)),

      or alternatively, elementwise Theano function.

      (see [activations](../activations.md)).

      If you don't specify anything, no activation is applied

      (ie. "linear" activation: a(x) = x).

    weights: list of Numpy arrays to set as initial weights.

  # Arguments

    epsilon: small float > 0. Fuzz parameter.

      Theano expects epsilon >= 1e-5.

    mode: integer, 0, 1 or 2.

      - 0: feature-wise normalization.

          Each feature map in the input will

          be normalized separately. The axis on which

          to normalize is specified by the `axis` argument.

          Note that if the input is a 4D image tensor

          using Theano conventions (samples, channels, rows, cols)

          then you should set `axis` to `1` to normalize along

          the channels axis.

          During training we use per-batch statistics to normalize

          the data, and during testing we use running averages

          computed during the training phase.

      Note that the order of this list is [gamma, beta, mean, std]

    beta_init: name of initialization function for shift parameter

      (see [initializations](../initializations.md)), or alternatively,

      Theano/TensorFlow function to use for weights initialization.

      TensorFlow function to use for weights initialization.

      This parameter is only relevant if you don't pass a `weights` argument.

    gamma_init: name of initialization function for scale parameter (see

      [initializations](../initializations.md)), or alternatively,

      Theano/TensorFlow function to use for weights initialization.

      TensorFlow function to use for weights initialization.

      This parameter is only relevant if you don't pass a `weights` argument.

    gamma_regularizer: instance of [WeightRegularizer](../regularizers.md)

      (eg. L1 or L2 regularization), applied to the gamma vector.