### `tf.sparse_placeholder(dtype, shape=None, name=None)` {#sparse_placeholder}
Inserts a placeholder for a sparse tensor that will be always fed.
**Important**: This sparse tensor will produce an error if evaluated.
Its value must be fed using the `feed_dict` optional argument to
`Session.run()`, `Tensor.eval()`, or `Operation.run()`.
For example:
```python
x = tf.sparse_placeholder(tf.float32)
y = tf.sparse_reduce_sum(x)
with tf.Session() as sess:
print(sess.run(y)) # ERROR: will fail because x was not fed.
indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
values = np.array([1.0, 2.0], dtype=np.float32)
shape = np.array([7, 9, 2], dtype=np.int64)
print(sess.run(y, feed_dict={
x: tf.SparseTensorValue(indices, values, shape)})) # Will succeed.
print(sess.run(y, feed_dict={
x: (indices, values, shape)})) # Will succeed.
sp = tf.SparseTensor(indices=indices, values=values, dense_shape=shape)
sp_value = sp.eval(session)
print(sess.run(y, feed_dict={x: sp_value})) # Will succeed.
```
##### Args:
* `dtype`: The type of `values` elements in the tensor to be fed.
* `shape`: The shape of the tensor to be fed (optional). If the shape is not
specified, you can feed a sparse tensor of any shape.
* `name`: A name for prefixing the operations (optional).
##### Returns:
A `SparseTensor` that may be used as a handle for feeding a value, but not
evaluated directly.