def split(split_dim, num_split, value, name="split"):
"""Splits a tensor into `num_split` tensors along one dimension.
Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
Requires that `num_split` evenly divide `value.shape[split_dim]`.
For example:
```python
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(1, 3, value)
tf.shape(split0) ==> [5, 10]
```
Args:
split_dim: A 0-D `int32` `Tensor`. The dimension along which to split.
Must be in the range `[0, rank(value))`.
num_split: A Python integer. The number of ways to split.
value: The `Tensor` to split.
name: A name for the operation (optional).
Returns:
`num_split` `Tensor` objects resulting from splitting `value`.
"""
return gen_array_ops._split(split_dim=split_dim,
num_split=num_split,
value=value,
name=name)
def split(split_dim, num_split, value, name="split"):
"""Splits a tensor into `num_split` tensors along one dimension.
Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
Requires that `num_split` evenly divide `value.shape[split_dim]`.
For example:
```python
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(1, 3, value)
tf.shape(split0) ==> [5, 10]
```
Args:
split_dim: A 0-D `int32` `Tensor`. The dimension along which to split.
Must be in the range `[0, rank(value))`.
num_split: A Python integer. The number of ways to split.
value: The `Tensor` to split.
name: A name for the operation (optional).
Returns:
`num_split` `Tensor` objects resulting from splitting `value`.
"""
return gen_array_ops._split(split_dim=split_dim,
num_split=num_split,
value=value,
name=name)