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+### `tf.test.compute_gradient(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)` {#compute_gradient}
+
+Computes and returns the theoretical and numerical Jacobian.
+
+If `x` or `y` is complex, the Jacobian will still be real but the
+corresponding Jacobian dimension(s) will be twice as large.  This is required
+even if both input and output is complex since TensorFlow graphs are not
+necessarily holomorphic, and may have gradients not expressible as complex
+numbers.  For example, if `x` is complex with shape `[m]` and `y` is complex
+with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with
+
+    J[:m, :n] = d(Re y)/d(Re x)
+    J[:m, n:] = d(Im y)/d(Re x)
+    J[m:, :n] = d(Re y)/d(Im x)
+    J[m:, n:] = d(Im y)/d(Im x)
+
+##### Args:
+
+
+*  <b>`x`</b>: a tensor or list of tensors
+*  <b>`x_shape`</b>: the dimensions of x as a tuple or an array of ints. If x is a list,
+  then this is the list of shapes.
+
+*  <b>`y`</b>: a tensor
+*  <b>`y_shape`</b>: the dimensions of y as a tuple or an array of ints.
+*  <b>`x_init_value`</b>: (optional) a numpy array of the same shape as "x"
+    representing the initial value of x. If x is a list, this should be a list
+    of numpy arrays.  If this is none, the function will pick a random tensor
+    as the initial value.
+*  <b>`delta`</b>: (optional) the amount of perturbation.
+*  <b>`init_targets`</b>: list of targets to run to initialize model params.
+    TODO(mrry): remove this argument.
+
+##### Returns:
+
+  Two 2-d numpy arrays representing the theoretical and numerical
+  Jacobian for dy/dx. Each has "x_size" rows and "y_size" columns
+  where "x_size" is the number of elements in x and "y_size" is the
+  number of elements in y. If x is a list, returns a list of two numpy arrays.
+