code: def Input(
      shape=None,
      batch_size=None,
      dtype=None,
      sparse=None,
      batch_shape=None,
      name=None,
      tensor=None,
  ):
      """Used to instantiate a Keras tensor.
  
      A Keras tensor is a symbolic tensor-like object, 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)`
  
      Args:
          shape: A shape tuple (tuple of integers or `None` objects),
              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 and may vary (e.g. sequence length).
          batch_size: Optional static batch size (integer).
          dtype: The data type expected by the input, as a string
              (e.g. `"float32"`, `"int32"`...)
          sparse: A boolean specifying whether the expected input will be sparse
              tensors. Note that, if `sparse` is `False`, sparse tensors can still
              be passed into the input - they will be densified with a default
              value of 0. This feature is only supported with the TensorFlow
              backend. Defaults to `False`.
          name: 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.
          tensor: Optional existing tensor to wrap into the `Input` layer.
              If set, the layer will use this tensor rather
              than creating a new placeholder tensor.
  
      Returns:
        A Keras tensor.
  
      Example:
  
      ```python
      # This is a logistic regression in Keras
      x = Input(shape=(32,))
      y = Dense(16, activation='softmax')(x)
      model = Model(x, y)
      ```
      """
      layer = InputLayer(
          shape=shape,
          batch_size=batch_size,
          dtype=dtype,
          sparse=sparse,
          batch_shape=batch_shape,
          name=name,
          input_tensor=tensor,
      )
      return layer.output
  
file_path: keras/keras/layers/core/input_layer.py
imports: import warnings
  from keras import backend
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
  from keras.ops.node import Node
label_desc: Use image node_id 1 for calling a global function with example usage: Input(shape, batch_size, dtype, sparse, batch_shape, name, tensor) and returns: layer