# tff.analytics.heavy_hitters.iblt.IbltTensorEncoder

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/federated/blob/v0.87.0 Version 2.0, January 2004 Licensed under the Apache License, Version 2.0 (the) |

Encodes the strings into an IBLT data structure.

Inherits From: [`IbltEncoder`](https://www.tensorflow.org/federated/api_docs/python/tff/analytics/heavy_hitters/iblt/IbltEncoder)

    tff.analytics.heavy_hitters.iblt.IbltTensorEncoder(
        value_shape: Sequence[int], *args, **kwargs
    )

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| ## Args ||
|---|---|
| `value_shape` | Shape of the values. |
| `*args` | See IbltEncoder. |
| `**kwargs` | See IbltEncoder. |

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## Methods

### `compute_chunks`

[View source](https://github.com/tensorflow/federated/blob/v0.87.0
Version 2.0, January 2004
Licensed under the Apache License, Version 2.0 (the)

    compute_chunks(
        input_strings
    )

Returns Tensor containing integer chunks for input strings.

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| Args ||
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| `input_strings` | A tensor of strings. |

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| Returns ||
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| A 2D tensor with rows consisting of integer chunks corresponding to the string indexed by the row and a trimmed `input_strings` that can fit in the IBLT. ||

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### `compute_iblt`

[View source](https://github.com/tensorflow/federated/blob/v0.87.0
Version 2.0, January 2004
Licensed under the Apache License, Version 2.0 (the)

    @tf.function
    compute_iblt(
        input_strings: tf.Tensor, input_values: tf.Tensor
    ) -> tuple[tf.Tensor, tf.Tensor]

Returns Tensor containing the values of the IBLT data structure.

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| Args ||
|---|---|
| `input_strings` | A 1D tensor of strings. |
| `input_values` | A tensor of shape `(num_input_strings, value_shape)` containing values for each string. |

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| Returns ||
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| A tuple of tensors where the first one is of shape `[repetitions, table_size, num_chunks+2]` whose value at index `(r, h, c)` corresponds to chunk `c` of the keys if `c < num_chunks`, to the counts if `c == num_chunks`, and to the checks if `c == num_chunks + 1`. The second one is of shape `[repetitions, table_size, product(value_shape)]` and contains the tensor values at each key. ||

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