# tf.quantized_concat(concat_dim, values, input_mins, input_maxes, name=None)

### tf.quantized_concat(concat_dim, values, input_mins, input_maxes, name=None)

See the guide: Tensor Transformations > Slicing and Joining

Concatenates quantized tensors along one dimension.

#### Args:

• concat_dim: A Tensor of type int32. 0-D. The dimension along which to concatenate. Must be in the range [0, rank(values)).
• values: A list of at least 2 Tensor objects of the same type. The N Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except concat_dim.
• input_mins: A list with the same number of Tensor objects as values of Tensor objects of type float32. The minimum scalar values for each of the input tensors.
• input_maxes: A list with the same number of Tensor objects as values of Tensor objects of type float32. The maximum scalar values for each of the input tensors.
• name: A name for the operation (optional).

#### Returns:

A tuple of Tensor objects (output, output_min, output_max). * output: A Tensor. Has the same type as values. A Tensor with the concatenation of values stacked along the concat_dim dimension. This tensor's shape matches that of values except in concat_dim where it has the sum of the sizes. * output_min: A Tensor of type float32. The float value that the minimum quantized output value represents. * output_max: A Tensor of type float32. The float value that the maximum quantized output value represents.

Defined in tensorflow/python/ops/gen_array_ops.py.