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import tensorflow as tf
help(tf.concat)


    

    




Help on function concat in module tensorflow.python.ops.array_ops:

concat(values, axis, name='concat')
    Concatenates tensors along one dimension.
    
    Concatenates the list of tensors `values` along dimension `axis`.  If
    `values[i].shape = [D0, D1, ... Daxis(i), ...Dn]`, the concatenated
    result has shape
    
        [D0, D1, ... Raxis, ...Dn]
    
    where
    
        Raxis = sum(Daxis(i))
    
    That is, the data from the input tensors is joined along the `axis`
    dimension.
    
    The number of dimensions of the input tensors must match, and all dimensions
    except `axis` must be equal.
    
    For example:
    
    ```python
    t1 = [[1, 2, 3], [4, 5, 6]]
    t2 = [[7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 0) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 1) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
    
    # tensor t3 with shape [2, 3]
    # tensor t4 with shape [2, 3]
    tf.shape(tf.concat([t3, t4], 0)) ==> [4, 3]
    tf.shape(tf.concat([t3, t4], 1)) ==> [2, 6]
    ```
    
    Note: If you are concatenating along a new axis consider using stack.
    E.g.
    
    ```python
    tf.concat([tf.expand_dims(t, axis) for t in tensors], axis)
    ```
    
    can be rewritten as
    
    ```python
    tf.stack(tensors, axis=axis)
    ```
    
    Args:
      values: A list of `Tensor` objects or a single `Tensor`.
      axis: 0-D `int32` `Tensor`.  Dimension along which to concatenate.
      name: A name for the operation (optional).
    
    Returns:
      A `Tensor` resulting from concatenation of the input tensors.

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