parameters: (shape=None, ndim=None, dtype=None, sparse=False, name=None)
return_statement: return x
docstring: Instantiates a placeholder tensor and returns it.
  
    Arguments:
        shape: Shape of the placeholder
            (integer tuple, may include `None` entries).
        ndim: Number of axes of the tensor.
            At least one of {`shape`, `ndim`} must be specified.
            If both are specified, `shape` is used.
        dtype: Placeholder type.
        sparse: Boolean, whether the placeholder should have a sparse type.
        name: Optional name string for the placeholder.
  
    Returns:
        Tensor instance (with Keras metadata included).
  
    Examples:
    ```python
        >>> from keras import backend as K
        >>> input_ph = K.placeholder(shape=(2, 4, 5))
        >>> input_ph
        <tf.Tensor 'Placeholder_4:0' shape=(2, 4, 5) dtype=float32>
    ```
docstring_summary: Instantiates a placeholder tensor and returns it.
docstring_tokens: Instantiates a placeholder tensor and returns it .
function: def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
    """Instantiates a placeholder tensor and returns it.
  
    Arguments:
        shape: Shape of the placeholder
            (integer tuple, may include `None` entries).
        ndim: Number of axes of the tensor.
            At least one of {`shape`, `ndim`} must be specified.
            If both are specified, `shape` is used.
        dtype: Placeholder type.
        sparse: Boolean, whether the placeholder should have a sparse type.
        name: Optional name string for the placeholder.
  
    Returns:
        Tensor instance (with Keras metadata included).
  
    Examples:
    ```python
        >>> from keras import backend as K
        >>> input_ph = K.placeholder(shape=(2, 4, 5))
        >>> input_ph
        <tf.Tensor 'Placeholder_4:0' shape=(2, 4, 5) dtype=float32>
    ```
    """
    if dtype is None:
      dtype = floatx()
    if not shape:
      if ndim:
        shape = tuple([None for _ in range(ndim)])
    if sparse:
      x = array_ops.sparse_placeholder(dtype, shape=shape, name=name)
    else:
      x = array_ops.placeholder(dtype, shape=shape, name=name)
    x._uses_learning_phase = False
    return x
function_tokens: def placeholder ( shape = None , ndim = None , dtype = None , sparse = False , name = None ) : if dtype is None : dtype = floatx ( ) if not shape : if ndim : shape = tuple ( [ None for _ in range ( ndim ) ] ) if sparse : x = array_ops . sparse_placeholder ( dtype , shape = shape , name = name ) else : x = array_ops . placeholder ( dtype , shape = shape , name = name ) x . _uses_learning_phase = False return x