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# tf.sparse.expand_dims

Returns a tensor with an length 1 axis inserted at index `axis`.

Given a tensor `input`, this operation inserts a dimension of length 1 at the dimension index `axis` of `input`'s shape. The dimension index follows python indexing rules: It's zero-based, a negative index it is counted backward from the end.

This operation is useful to:

• Add an outer "batch" dimension to a single element.
• To add an inner vector length axis to a tensor of scalars.

#### For example:

If you have a sparse tensor with shape `[height, width, depth]`:

````sp = tf.sparse.SparseTensor(indices=[[3,4,1]], values=[7,],`
`                            dense_shape=[10,10,3])`
```

You can add an outer `batch` axis by passing `axis=0`:

````tf.sparse.expand_dims(sp, axis=0).shape.as_list()`
`[1, 10, 10, 3]`
```

The new axis location matches Python `list.insert(axis, 1)`:

````tf.sparse.expand_dims(sp, axis=1).shape.as_list()`
`[10, 1, 10, 3]`
```

Following standard python indexing rules, a negative `axis` counts from the end so `axis=-1` adds an inner most dimension:

````tf.sparse.expand_dims(sp, axis=-1).shape.as_list()`
`[10, 10, 3, 1]`
```
````sp.shape.as_list()`
`[10, 10, 3]`
`tf.sparse.expand_dims(sp).shape.as_list()`
`[10, 10, 3, 1]`
```

This operation requires that `axis` is a valid index for `input.shape`, following python indexing rules:

``````-1-tf.rank(input) <= axis <= tf.rank(input)
``````

This operation is related to:

`sp_input` A `SparseTensor`.
`axis` 0-D (scalar). Specifies the dimension index at which to expand the shape of `input`. Must be in the range ```[-rank(sp_input) - 1, rank(sp_input)]```. Defaults to `-1`.
`name` The name of the output `SparseTensor`.

A `SparseTensor` with the same data as `sp_input`, but its shape has an additional dimension of size 1 added.

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