tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient
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Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.

Compat aliases for migration

tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(
    gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)

Args
`gradients`	A `Tensor` of type `float32`. Backpropagated gradients above the FakeQuantWithMinMaxVars operation, shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`.
`inputs`	A `Tensor` of type `float32`. Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape same as `gradients`. min, max: Quantization interval, floats of shape `[d]`.
`min`	A `Tensor` of type `float32`.
`max`	A `Tensor` of type `float32`.
`num_bits`	An optional `int`. Defaults to `8`. The bitwidth of the quantization; between 2 and 16, inclusive.
`narrow_range`	An optional `bool`. Defaults to `False`. Whether to quantize into 2^num_bits - 1 distinct values.
`name`	A name for the operation (optional).

Returns
A tuple of `Tensor` objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).
`backprops_wrt_input`	A `Tensor` of type `float32`.
`backprop_wrt_min`	A `Tensor` of type `float32`.
`backprop_wrt_max`	A `Tensor` of type `float32`.

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